Merge branch 'main' into develop-pushcollabonly

README.md

 
 # Unity ML-Agents Toolkit
 
-[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/)
 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
 
 ## Releases & Documentation
 
 
-**Our latest, stable release is `Release 14`. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)
+**Our latest, stable release is `Release 13`. Click
+[here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `main` branch which is
 | **Version** | **Release Date** | **Source** | **Documentation** | **Download** | **Python Package** | **Unity Package** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|:------------:|:------------:|
 | **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) | -- | -- |
-| **Release 14** | **March 5, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_14)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_14.zip)** | **[0.24.1](https://pypi.org/project/mlagents/0.24.1/)** | **[1.8.1](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.8/manual/index.html)** |
-| **Verified Package 1.0.7** | **March 8, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.7)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.7.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.7](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** |
-| **Verified Package 1.0.6** | November 16, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.6) | [docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.6.zip) | [0.16.1](https://pypi.org/project/mlagents/0.16.1/) | [1.0.6](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html) |
+| **Verified Package 1.0.6** | **November 16, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.6)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.6.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.6](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** |
+| **Verified Package 1.0.5** | September 23, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.5) | [docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.5.zip) | [0.16.1](https://pypi.org/project/mlagents/0.16.1/) | [1.0.5](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html) |
 
 If you are a researcher interested in a discussion of Unity as an AI platform,
 see a pre-print of our

utils/validate_versions.py

 
 
 def set_version(
-    python_version: str,
-    csharp_version: str,
-    csharp_extensions_version: str,
-    release_tag: Optional[str],
+    python_version: str, csharp_version: str, release_tag: Optional[str]
 ) -> None:
     # Sanity check - make sure test tags have a test or dev version
     if release_tag and "test" in release_tag:
 
     if csharp_version is not None:
         package_version = csharp_version + "-preview"
-        if csharp_extensions_version is not None:
-            extension_version = csharp_extensions_version + "-preview"
-        set_extension_package_version(package_version, extension_version)
+        set_extension_package_version(package_version)
         print(f"Setting package version to {package_version} in {ACADEMY_PATH}")
         set_academy_version_string(package_version)
 
         f.write("\n")
 
 
-def set_extension_package_version(
-    new_dependency_version: str, new_extension_version
-) -> None:
+def set_extension_package_version(new_version: str) -> None:
-    package_json["dependencies"]["com.unity.ml-agents"] = new_dependency_version
-    if new_extension_version is not None:
-        package_json["version"] = new_extension_version
+    package_json["dependencies"]["com.unity.ml-agents"] = new_version
     with open(MLAGENTS_EXTENSIONS_PACKAGE_JSON_PATH, "w") as f:
         json.dump(package_json, f, indent=2)
         f.write("\n")
     parser = argparse.ArgumentParser()
     parser.add_argument("--python-version", default=None)
     parser.add_argument("--csharp-version", default=None)
-    parser.add_argument("--csharp-extensions-version", default=None)
     parser.add_argument("--release-tag", default=None)
     # unused, but allows precommit to pass filenames
     parser.add_argument("files", nargs="*")
         print(f"Updating python library to version {args.python_version}")
         if args.csharp_version:
             print(f"Updating C# package to version {args.csharp_version}")
-        if args.csharp_extensions_version:
-            print(
-                f"Updating C# extensions package to version {args.csharp_extensions_version}"
-            )
-        set_version(
-            args.python_version,
-            args.csharp_version,
-            args.csharp_extensions_version,
-            args.release_tag,
-        )
+        set_version(args.python_version, args.csharp_version, args.release_tag)
         if args.release_tag is not None:
             print_release_tag_commands(
                 args.python_version, args.csharp_version, args.release_tag

utils/make_readme_table.py

     ReleaseInfo("release_11", "1.7.0", "0.23.0", "December 21, 2020"),
     ReleaseInfo("release_12", "1.7.2", "0.23.0", "December 22, 2020"),
     ReleaseInfo("release_13", "1.8.0", "0.24.0", "February 17, 2021"),
-    ReleaseInfo("release_14", "1.8.1", "0.24.1", "March 5, 2021"),
-    ReleaseInfo("", "1.0.7", "0.16.1", "March 8, 2021", is_verified=True),
     ReleaseInfo("", "1.0.6", "0.16.1", "November 16, 2020", is_verified=True),
     ReleaseInfo("", "1.0.5", "0.16.1", "September 23, 2020", is_verified=True),
     ReleaseInfo("", "1.0.4", "0.16.1", "August 20, 2020", is_verified=True),

utils/validate_release_links.py

 from typing import List, Optional, Pattern
 
 RELEASE_PATTERN = re.compile(r"release_[0-9]+(_docs)*")
-# This matches the various ways to invoke pip: "pip", "pip3", "python -m pip"
-# It matches "mlagents" and "mlagents_envs", accessible as group "package"
-# and optionally matches the version, e.g. "==1.2.3"
-PIP_INSTALL_PATTERN = re.compile(
-    r"(python -m )?pip3* install (?P<package>mlagents(_envs)?)(==[0-9]\.[0-9]\.[0-9](\.dev[0-9]+)?)?"
-)
-# To allow everything in the file (effectively skipping it), use MATCH_ANY for the value
+# To allow everything in the file, use None for the value
 ALLOW_LIST = {
     # Previous release table
     "README.md": re.compile(r"\*\*(Verified Package ([0-9]\.?)*|Release [0-9]+)\*\*"),
 }
 
 
-def test_release_pattern():
-    # Just some sanity check that the regex works as expected.
-    for s, expected in [
-        (
-            "https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/Food.md",
-            True,
-        ),
-        ("https://github.com/Unity-Technologies/ml-agents/blob/release_4/Foo.md", True),
-        (
-            "git clone --branch release_4 https://github.com/Unity-Technologies/ml-agents.git",
-            True,
-        ),
-        (
-            "https://github.com/Unity-Technologies/ml-agents/blob/release_123_docs/Foo.md",
-            True,
-        ),
-        (
-            "https://github.com/Unity-Technologies/ml-agents/blob/release_123/Foo.md",
-            True,
-        ),
-        (
-            "https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Foo.md",
-            False,
-        ),
-    ]:
-        assert bool(RELEASE_PATTERN.search(s)) is expected
-
-    print("release tests OK!")
-
-
-def test_pip_pattern():
+def test_pattern():
-    for s, expected in [
-        ("pip install mlagents", True),
-        ("pip3 install mlagents", True),
-        ("python -m pip install mlagents", True),
-        ("python -m pip install mlagents==1.2.3", True),
-        ("python -m pip install mlagents_envs==1.2.3", True),
-    ]:
-        assert bool(PIP_INSTALL_PATTERN.search(s)) is expected
-
-    sub_expected = "Try running rm -rf / to install"
-    assert sub_expected == PIP_INSTALL_PATTERN.sub(
-        "rm -rf /", "Try running python -m pip install mlagents==1.2.3 to install"
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/Food.md"
-
-    print("pip tests OK!")
-
-
-def update_pip_install_line(line, package_verion):
-    match = PIP_INSTALL_PATTERN.search(line)
-    package_name = match.group("package")
-    replacement_version = f"python -m pip install {package_name}=={package_verion}"
-    updated = PIP_INSTALL_PATTERN.sub(replacement_version, line)
-    return updated
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_4/Foo.md"
+    )
+    assert RELEASE_PATTERN.search(
+        "git clone --branch release_4 https://github.com/Unity-Technologies/ml-agents.git"
+    )
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_123_docs/Foo.md"
+    )
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_123/Foo.md"
+    )
+    assert not RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Foo.md"
+    )
+    print("tests OK!")
 
 
 def git_ls_files() -> List[str]:
     raise RuntimeError("Can't determine release tag")
 
 
-def get_python_package_version() -> str:
-    """
-    Returns the mlagents python package.
-    :return:
-    """
-    with open(TRAINER_INIT_FILE) as f:
-        for line in f:
-            if "__version__" in line:
-                lhs, equals_string, rhs = line.strip().partition(" = ")
-                # Evaluate the right hand side of the expression
-                return ast.literal_eval(rhs)
-    # If we couldn't find the release tag, raise an exception
-    # (since we can't return None here)
-    raise RuntimeError("Can't determine python package version")
-
-
-    filename: str,
-    release_tag_pattern: Pattern,
-    release_tag: str,
-    pip_allow_pattern: Pattern,
-    package_version: str,
+    filename: str, global_allow_pattern: Pattern, release_tag: str
 ) -> List[str]:
     """
     Validate a single file and return any offending lines.
             allow_list_pattern = ALLOW_LIST.get(filename, None)
             with open(filename) as f:
                 for line in f:
-                    # Does it contain anything of the form release_123
-                    has_release_pattern = RELEASE_PATTERN.search(line) is not None
-                    # Does it contain this particular release, e.g. release_42 or release_42_docs
-                    has_release_tag_pattern = (
-                        release_tag_pattern.search(line) is not None
-                    )
-                    # Does it contain the allow list pattern for the file (if there is one)
-                    has_allow_list_pattern = (
-                        allow_list_pattern
+                    keep_line = True
+                    keep_line = not RELEASE_PATTERN.search(line)
+                    keep_line |= global_allow_pattern.search(line) is not None
+                    keep_line |= (
+                        allow_list_pattern is not None
-                    pip_install_ok = (
-                        has_allow_list_pattern
-                        or PIP_INSTALL_PATTERN.search(line) is None
-                        or pip_allow_pattern.search(line) is not None
-                    )
-
-                    release_tag_ok = (
-                        not has_release_pattern
-                        or has_release_tag_pattern
-                        or has_allow_list_pattern
-                    )
-
-                    if release_tag_ok and pip_install_ok:
+                    if keep_line:
-                        new_line = re.sub(r"release_[0-9]+", fr"{release_tag}", line)
-                        new_line = update_pip_install_line(new_line, package_version)
-                        new_file.write(new_line)
+                        new_file.write(
+                            re.sub(r"release_[0-9]+", fr"{release_tag}", line)
+                        )
         if bad_lines:
             if os.path.exists(filename):
                 os.remove(filename)
 
 
-def check_all_files(
-    release_allow_pattern: Pattern,
-    release_tag: str,
-    pip_allow_pattern: Pattern,
-    package_version: str,
-) -> List[str]:
+def check_all_files(allow_pattern: Pattern, release_tag: str) -> List[str]:
-    :param release_allow_pattern:
+    :param allow_pattern:
     """
     bad_lines = []
     file_types = {".py", ".md", ".cs"}
-        bad_lines += check_file(
-            file_name,
-            release_allow_pattern,
-            release_tag,
-            pip_allow_pattern,
-            package_version,
-        )
+        bad_lines += check_file(file_name, allow_pattern, release_tag)
     return bad_lines
 
 
         print("Release tag is None, exiting")
         sys.exit(0)
 
-    package_version = get_python_package_version()
-    print(f"Python package version: {package_version}")
-    release_allow_pattern = re.compile(f"{release_tag}(_docs)?")
-    pip_allow_pattern = re.compile(
-        f"python -m pip install mlagents(_envs)?=={package_version}"
-    )
-    bad_lines = check_all_files(
-        release_allow_pattern, release_tag, pip_allow_pattern, package_version
-    )
+    allow_pattern = re.compile(f"{release_tag}(_docs)*")
+    bad_lines = check_all_files(allow_pattern, release_tag)
     if bad_lines:
         for line in bad_lines:
             print(line)
 
 if __name__ == "__main__":
     if "--test" in sys.argv:
-        test_release_pattern()
-        test_pip_pattern()
+        test_pattern()
     main()

.github/workflows/pytest.yml

         TEST_ENFORCE_BUFFER_KEY_TYPES: 1
     strategy:
       matrix:
-        python-version: [3.6.x, 3.7.x, 3.8.x, 3.9.x]
-        include:
-          - python-version: 3.6.x
-            pip_constraints: test_constraints_min_version.txt
-          - python-version: 3.7.x
-            pip_constraints: test_constraints_mid_version.txt
-          - python-version: 3.8.x
-            pip_constraints: test_constraints_mid_version.txt
-          - python-version: 3.9.x
-            pip_constraints: test_constraints_max_version.txt
+        python-version: [3.6.x, 3.7.x, 3.8.x]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python
         # This path is specific to Ubuntu
         path: ~/.cache/pip
         # Look to see if there is a cache hit for the corresponding requirements file
-        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'gym-unity/setup.py', 'test_requirements.txt', matrix.pip_constraints) }}
+        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'gym-unity/setup.py', 'test_requirements.txt') }}
         restore-keys: |
           ${{ runner.os }}-pip-
           ${{ runner.os }}-
       run: |
         python -m pip install --upgrade pip
         python -m pip install --upgrade setuptools
-        python -m pip install --progress-bar=off -e ./ml-agents-envs -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -e ./ml-agents -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -r test_requirements.txt -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -e ./gym-unity -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -e ./ml-agents-plugin-examples -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents-envs
+        python -m pip install --progress-bar=off -e ./ml-agents
+        python -m pip install --progress-bar=off -r test_requirements.txt
+        python -m pip install --progress-bar=off -e ./gym-unity
+        python -m pip install --progress-bar=off -e ./ml-agents-plugin-examples
     - name: Save python dependencies
       run: |
         pip freeze > pip_versions-${{ matrix.python-version }}.txt

.yamato/com.unity.ml-agents-pack.yml

     image: package-ci/ubuntu:stable
     flavor: b1.small
   commands:
-    - |
-      python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
-      unity-downloader-cli -u 2018.4 -c editor --wait --fast
-      ./.Editor/Unity -projectPath Project -batchMode -executeMethod Unity.MLAgents.SampleExporter.ExportCuratedSamples -logFile -
-      npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-      upm-ci project pack --project-path Project
+    - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci project pack --project-path Project
   artifacts:
     packages:
       paths:

.yamato/python-ll-api-test.yml

       python ml-agents/tests/yamato/scripts/run_llapi.py
       python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Basic
       python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-WallJump
-      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Match3
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Bouncer
   dependencies:
     - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:

.yamato/standalone-build-test.yml

     - unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity
-    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedGrid/TestGridCompressed.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity

DevProject/Packages/packages-lock.json

       "url": "https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-candidates"
     },
     "com.unity.barracuda": {
-      "version": "1.3.1-preview",
+      "version": "1.3.0-preview",
       "depth": 1,
       "source": "registry",
       "dependencies": {
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.barracuda": "1.3.1-preview",
+        "com.unity.barracuda": "1.3.0-preview",
         "com.unity.modules.imageconversion": "1.0.0",
         "com.unity.modules.jsonserialize": "1.0.0",
         "com.unity.modules.physics": "1.0.0",
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.ml-agents": "1.8.0-preview"
+        "com.unity.ml-agents": "1.7.2-preview"
       }
     },
     "com.unity.multiplayer-hlapi": {

docs/Training-on-Microsoft-Azure.md

    instance, and set it as the working directory.
 2. Install the required packages:
    Torch: `pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html` and
-   MLAgents: `python -m pip install mlagents==0.24.1`
+   MLAgents: `pip3 install mlagents`
 
 ## Testing
 

docs/Learning-Environment-Design-Agents.md

 training process learns to control the speed of the Agent through this
 parameter.
 
-The [3DBall example](Learning-Environment-Examples.md#3dball-3d-balance-ball) uses
+The [Reacher example](Learning-Environment-Examples.md#reacher) uses
-![3DBall](images/balance.png)
+![reacher](images/reacher.png)
-These control values are applied as rotation to the cube:
+These control values are applied as torques to the bodies making up the arm:
-        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);
-        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);
+        var torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f) * 150f;
+        var torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f) * 150f;
+        m_RbA.AddTorque(new Vector3(torqueX, 0f, torqueZ));
-        gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-        gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
+        torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[2], -1f, 1f) * 150f;
+        torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[3], -1f, 1f) * 150f;
+        m_RbB.AddTorque(new Vector3(torqueX, 0f, torqueZ));
     }
 ```
 

docs/Training-Configuration-File.md

 | `network_settings -> hidden_units`           | (default = `128`) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. <br><br> Typical range: `32` - `512`                                                                                                                                                                                                                                                                                    |
 | `network_settings -> num_layers`             | (default = `2`) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. <br><br> Typical range: `1` - `3`                                                                                                                                                                                                                                                                                                                                                    |
 | `network_settings -> normalize`              | (default = `false`) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems.                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| `network_settings -> vis_encode_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
+| `network_settings -> vis_encoder_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
 
 
 ## Trainer-specific Configurations

docs/Training-ML-Agents.md

 
     # Configuration of the neural network (common to PPO/SAC)
     network_settings:
-      vis_encode_type: simple
+      vis_encoder_type: simple
       normalize: false
       hidden_units: 128
       num_layers: 2

docs/Installation-Anaconda-Windows.md

 the ml-agents Conda environment by typing `activate ml-agents`)_:
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
+The `--branch release_13` option will switch to the tag of the latest stable
 release. Omitting that will get the `main` branch which is potentially
 unstable.
 
 connected to the Internet and then type in the Anaconda Prompt:
 
 ```console
-python -m pip install mlagents==0.24.1
+pip install mlagents
 ```
 
 This will complete the installation of all the required Python packages to run
 this, you can try:
 
 ```console
-python -m pip install mlagents==0.24.1 --no-cache-dir
+pip install mlagents --no-cache-dir
 ```
 
 This `--no-cache-dir` tells the pip to disable the cache.

docs/Installation.md

 of our tutorials / guides assume you have access to our example environments).
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
-release. Omitting that will get the `main` branch which is potentially unstable.
+The `--branch release_13` option will switch to the tag of the latest stable
+release. Omitting that will get the `main` branch which is potentially
+unstable.
-back, make sure to clone the `main` branch (by omitting `--branch release_14`
+back, make sure to clone the `main` branch (by omitting `--branch release_13`
 from the command above). See our
 [Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
 information on contributing to the ML-Agents Toolkit.
 run from the command line:
 
 ```sh
-python -m pip install mlagents==0.24.1
+pip3 install mlagents
 ```
 
 Note that this will install `mlagents` from PyPi, _not_ from the cloned

docs/Learning-Environment-Examples.md

   number of goals.
 - Benchmark Mean Reward: 0.8
 
+## Tennis
+
+![Tennis](images/tennis.png)
+
+- Set-up: Two-player game where agents control rackets to hit a ball over the
+  net.
+- Goal: The agents must hit the ball so that the opponent cannot hit a valid
+  return.
+- Agents: The environment contains two agent with same Behavior Parameters.
+  After training you can set the `Behavior Type` to `Heuristic Only` on one of
+  the Agent's Behavior Parameters to play against your trained model.
+- Agent Reward Function (independent):
+  - +1.0 To the agent that wins the point. An agent wins a point by preventing
+    the opponent from hitting a valid return.
+  - -1.0 To the agent who loses the point.
+- Behavior Parameters:
+  - Vector Observation space: 9 variables corresponding to position, velocity
+    and orientation of ball and racket.
+  - Actions: 3 continuous actions, corresponding to movement
+    toward net or away from net, jumping and rotation.
+  - Visual Observations: None
+- Float Properties: Three
+  - gravity: Magnitude of gravity
+    - Default: 9.81
+    - Recommended Minimum: 6
+    - Recommended Maximum: 20
+  - scale: Specifies the scale of the ball in the 3 dimensions (equal across the
+    three dimensions)
+    - Default: .5
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 5
+
 ## Push Block
 
 ![Push](images/push.png)
     block).
   - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
     and counterclockwise, move along four different face directions, or do nothing.
+  - Visual Observations (Optional): One first-person camera. Use
+    `VisualPushBlock` scene. **The visual observation version of this
+    environment does not train with the provided default training parameters.**
 - Float Properties: Four
   - block_scale: Scale of the block along the x and z dimensions
     - Default: 2
 - Float Properties: Four
 - Benchmark Mean Reward (Big & Small Wall): 0.8
 
+## Reacher
+
+![Reacher](images/reacher.png)
+
+- Set-up: Double-jointed arm which can move to target locations.
+- Goal: The agents must move its hand to the goal location, and keep it there.
+- Agents: The environment contains 10 agent with same Behavior Parameters.
+- Agent Reward Function (independent):
+  - +0.1 Each step agent's hand is in goal location.
+- Behavior Parameters:
+  - Vector Observation space: 26 variables corresponding to position, rotation,
+    velocity, and angular velocities of the two arm rigid bodies.
+  - Actions: 4 continuous actions, corresponding to torque
+    applicable to two joints.
+  - Visual Observations: None.
+- Float Properties: Five
+  - goal_size: radius of the goal zone
+    - Default: 5
+    - Recommended Minimum: 1
+    - Recommended Maximum: 10
+  - goal_speed: speed of the goal zone around the arm (in radians)
+    - Default: 1
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 4
+  - gravity
+    - Default: 9.81
+    - Recommended Minimum: 4
+    - Recommended Maximum: 20
+  - deviation: Magnitude of sinusoidal (cosine) deviation of the goal along the
+    vertical dimension
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 5
+  - deviation_freq: Frequency of the cosine deviation of the goal along the
+    vertical dimension
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 3
+- Benchmark Mean Reward: 30
+
 ## Crawler
 
 ![Crawler](images/crawler.png)
+  - `CrawlerDynamicTarget`- Goal direction is randomized.
+  - `CrawlerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
+  - `CrawlerStaticTarget` - Goal direction is always forward.
+  - `CrawlerStaticVariableSpeed`- Goal direction is always forward. Walking speed is randomized
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward: 3000
+- Benchmark Mean Reward for `CrawlerDynamicTarget`: 2000
+- Benchmark Mean Reward for `CrawlerDynamicVariableSpeed`: 3000
+- Benchmark Mean Reward for `CrawlerStaticTarget`: 4000
+- Benchmark Mean Reward for `CrawlerStaticVariableSpeed`: 4000
 
 ## Worm
 
 - Goal: The agents must move its body toward the goal direction.
+  - `WormStaticTarget` - Goal direction is always forward.
+  - `WormDynamicTarget`- Goal direction is randomized.
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward: 800
+- Benchmark Mean Reward for `WormStaticTarget`: 1200
+- Benchmark Mean Reward for `WormDynamicTarget`: 800
 
 ## Food Collector
 
   - -1 for interaction with red spheres
 - Behavior Parameters:
   - Vector Observation space: 53 corresponding to velocity of agent (2), whether
-    agent is frozen and/or shot its laser (2), plus grid based perception of
-    objects around agent's forward direction (40 by 40 with 6 different categories).
+    agent is frozen and/or shot its laser (2), plus ray-based perception of
+    objects around agent's forward direction (49; 7 raycast angles with 7
+    measurements for each).
   - Actions:
     - 3 continuous actions correspond to Forward Motion, Side Motion and Rotation
     - 1 discrete acion branch for Laser with 2 possible actions corresponding to
     objects, goals, and walls.
   - Actions: 1 discrete action Branch, with 4 actions corresponding to agent
     rotation and forward/backward movement.
+  - Visual Observations (Optional): First-person view for the agent. Use
+    `VisualHallway` scene. **The visual observation version of this environment
+    does not train with the provided default training parameters.**
+## Bouncer
+
+![Bouncer](images/bouncer.png)
+
+- Set-up: Environment where the agent needs on-demand decision making. The agent
+  must decide how perform its next bounce only when it touches the ground.
+- Goal: Catch the floating green cube. Only has a limited number of jumps.
+- Agents: The environment contains one agent.
+- Agent Reward Function (independent):
+  - +1 For catching the green cube.
+  - -1 For bouncing out of bounds.
+  - -0.05 Times the action squared. Energy expenditure penalty.
+- Behavior Parameters:
+  - Vector Observation space: 6 corresponding to local position of agent and
+    green cube.
+  - Actions: 3 continuous actions corresponding to agent force applied for
+    the jump.
+  - Visual Observations: None
+- Float Properties: Two
+  - target_scale: The scale of the green cube in the 3 dimensions
+    - Default: 150
+    - Recommended Minimum: 50
+    - Recommended Maximum: 250
+- Benchmark Mean Reward: 10
+
 ## Soccer Twos
 
 ![SoccerTwos](images/soccer.png)
   correspond to articulation of the following body-parts: hips, chest, spine,
   head, thighs, shins, feet, arms, forearms and hands.
 - Goal: The agents must move its body toward the goal direction without falling.
+  - `WalkerDynamic`- Goal direction is randomized.
+  - `WalkerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
+  - `WalkerStatic` - Goal direction is always forward.
+  - `WalkerStaticVariableSpeed` - Goal direction is always forward. Walking
+     speed is randomized
 - Agents: The environment contains 10 independent agents with same Behavior
   Parameters.
 - Agent Reward Function (independent):
     - Default: 8
     - Recommended Minimum: 3
     - Recommended Maximum: 20
-- Benchmark Mean Reward : 2500
+- Benchmark Mean Reward for `WalkerDynamic`: 2500
+- Benchmark Mean Reward for `WalkerDynamicVariableSpeed`: 2500
+- Benchmark Mean Reward for `WalkerStatic`: 3500
+- Benchmark Mean Reward for `WalkerStaticVariableSpeed`: 3500
+
 
 
 ## Pyramids
     state.
   - Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and
     forward/backward movement.
+  - Visual Observations (Optional): First-person camera per-agent. Us
+    `VisualPyramids` scene. **The visual observation version of this environment
+    does not train with the provided default training parameters.**
 - Float Properties: None
 - Benchmark Mean Reward: 1.75
 

docs/Training-on-Amazon-Web-Service.md

 2. Clone the ML-Agents repo and install the required Python packages
 
    ```sh
-   git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+   git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
    cd ml-agents/ml-agents/
    pip3 install -e .
    ```

docs/Unity-Inference-Engine.md

 loading expects certain conventions for constants and tensor names. While it is
 possible to construct a model that follows these conventions, we don't provide
 any additional help for this. More details can be found in
-[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
+[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
-[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
+[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
 
 If you wish to run inference on an externally trained model, you should use
 Barracuda directly, instead of trying to run it through ML-Agents.

config/imitation/CrawlerStatic.yaml

 behaviors:
-  Crawler:
+  CrawlerStatic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024
         learning_rate: 0.0003
         use_actions: false
         use_vail: false
-        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000
-      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
       steps: 50000
       strength: 0.5
       samples_per_update: 0

config/sac/CrawlerDynamic.yaml

 behaviors:
-  Crawler:
+  CrawlerDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/FoodCollector.yaml

       learning_rate: 0.0003
       learning_rate_schedule: constant
       batch_size: 256
-      buffer_size: 2048
+      buffer_size: 500000
       buffer_init_steps: 0
       tau: 0.005
       steps_per_update: 10.0
     network_settings:
       normalize: false
-      hidden_units: 256
-      num_layers: 1
+      hidden_units: 128
+      num_layers: 2
       vis_encode_type: simple
     reward_signals:
       extrinsic:
     max_steps: 2000000
     time_horizon: 64
-    summary_freq: 60000
-    threaded: true
+    summary_freq: 10000
+    threaded: true

config/sac/WalkerDynamic.yaml

 behaviors:
-  Walker:
+  WalkerDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/WormDynamic.yaml

 behaviors:
-  Worm:
+  WormDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/ppo/CrawlerDynamic.yaml

 behaviors:
-  Crawler:
+  CrawlerDynamic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048

config/ppo/FoodCollector.yaml

       learning_rate_schedule: linear
     network_settings:
       normalize: false
-      hidden_units: 256
-      num_layers: 1
+      hidden_units: 128
+      num_layers: 2
       vis_encode_type: simple
     reward_signals:
       extrinsic:

config/ppo/WormDynamic.yaml

 behaviors:
-  Worm:
+  WormDynamic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024

config/ppo/CrawlerDynamicVariableSpeed.yaml

 behaviors:
-  Walker:
+  CrawlerDynamicVariableSpeed:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048
         gamma: 0.995
         strength: 1.0
     keep_checkpoints: 5
-    max_steps: 30000000
+    max_steps: 10000000
     time_horizon: 1000
     summary_freq: 30000
     threaded: true

ml-agents/README.md

 Install the `mlagents` package with:
 
 ```sh
-python -m pip install mlagents==0.24.1
+pip3 install mlagents
 ```
 
 ## Usage & More Information

ml-agents/tests/yamato/scripts/run_llapi.py

         # Examine the number of observations per Agent
         print("Number of observations : ", len(group_spec.observation_specs))
 
-        for obs_spec in group_spec.observation_specs:
-            # Make sure the name was set in the ObservationSpec
-            assert bool(obs_spec.name) is True, f'obs_spec.name="{obs_spec.name}"'
-
         # Is there a visual observation ?
         vis_obs = any(
             len(obs_spec.shape) == 3 for obs_spec in group_spec.observation_specs

ml-agents/mlagents/trainers/buffer.py

         else:
             return return_data
 
-    @property
-    def contains_lists(self) -> bool:
-        """
-        Checks whether this AgentBufferField contains List[np.ndarray].
-        """
-        return len(self) > 0 and isinstance(self[0], list)
-
-    def append(self, element: BufferEntry, padding_value: float = 0.0) -> None:
+    def append(self, element: np.ndarray, padding_value: float = 0.0) -> None:
         """
         Adds an element to this list. Also lets you change the padding
         type, so that it can be set on append (e.g. action_masks should
                     " too large given the current number of data points."
                 )
             if batch_size * training_length > len(self):
-                if self.contains_lists:
-                    padding = []
-                else:
-                    # We want to duplicate the last value in the array, multiplied by the padding_value.
-                    padding = np.array(self[-1], dtype=np.float32) * self.padding_value
+                padding = np.array(self[-1], dtype=np.float32) * self.padding_value
                 return [padding] * (training_length - leftover) + self[:]
 
             else:
             dimension is equal to the length of the AgentBufferField.
         """
         if len(self) > 0 and not isinstance(self[0], list):
-            return np.asanyarray(self, dtype=dtype)
+            return np.asanyarray(self, dytpe=dtype)
 
         shape = None
         for _entry in self:

ml-agents/mlagents/trainers/optimizer/torch_optimizer.py

 
         # If we're using LSTM, we want to get all the intermediate memories.
         all_next_memories: Optional[AgentBufferField] = None
+        if self.policy.use_recurrent:
+            (
+                value_estimates,
+                all_next_memories,
+                next_memory,
+            ) = self._evaluate_by_sequence(current_obs, memory)
+        else:
+            value_estimates, next_memory = self.critic.critic_pass(
+                current_obs, memory, sequence_length=batch.num_experiences
+            )
-        # To prevent memory leak and improve performance, evaluate with no_grad.
-        with torch.no_grad():
-            if self.policy.use_recurrent:
-                (
-                    value_estimates,
-                    all_next_memories,
-                    next_memory,
-                ) = self._evaluate_by_sequence(current_obs, memory)
-            else:
-                value_estimates, next_memory = self.critic.critic_pass(
-                    current_obs, memory, sequence_length=batch.num_experiences
-                )
-
-        # Store the memory for the next trajectory. This should NOT have a gradient.
+        # Store the memory for the next trajectory
         self.critic_memory_dict[agent_id] = next_memory
 
         next_value_estimate, _ = self.critic.critic_pass(

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

         return update_stats
 
     def get_modules(self):
-        modules = {
-            "Optimizer:value_optimizer": self.optimizer,
-            "Optimizer:critic": self._critic,
-        }
+        modules = {"Optimizer": self.optimizer}
         for reward_provider in self.reward_signals.values():
             modules.update(reward_provider.get_modules())
         return modules

ml-agents/mlagents/trainers/ppo/trainer.py

             self.policy.update_normalization(agent_buffer_trajectory)
 
         # Get all value estimates
-        (
-            value_estimates,
-            value_next,
-            value_memories,
-        ) = self.optimizer.get_trajectory_value_estimates(
+        value_estimates, value_next, value_memories = self.optimizer.get_trajectory_value_estimates(
             agent_buffer_trajectory,
             trajectory.next_obs,
             trajectory.done_reached and not trajectory.interrupted,
             int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
         )
 
-        advantages = np.array(
-            self.update_buffer[BufferKey.ADVANTAGES].get_batch(), dtype=np.float32
-        )
+        advantages = np.array(self.update_buffer[BufferKey.ADVANTAGES].get_batch())
         self.update_buffer[BufferKey.ADVANTAGES].set(
             (advantages - advantages.mean()) / (advantages.std() + 1e-10)
         )

ml-agents/mlagents/trainers/sac/optimizer_torch.py

 
     def get_modules(self):
         modules = {
-            "Optimizer:q_network": self.q_network,
-            "Optimizer:value_network": self._critic,
+            "Optimizer:value_network": self.q_network,
             "Optimizer:target_network": self.target_network,
             "Optimizer:policy_optimizer": self.policy_optimizer,
             "Optimizer:value_optimizer": self.value_optimizer,

ml-agents/mlagents/trainers/tests/dummy_config.py

     shapes: List[Tuple[int, ...]]
 ) -> List[ObservationSpec]:
     obs_specs: List[ObservationSpec] = []
-    for i, shape in enumerate(shapes):
+    for shape in shapes:
-        spec = ObservationSpec(
-            name=f"observation {i} with shape {shape}",
-            shape=shape,
-            dimension_property=dim_prop,
-            observation_type=ObservationType.DEFAULT,
-        )
+        spec = ObservationSpec(shape, dim_prop, ObservationType.DEFAULT)
         obs_specs.append(spec)
     return obs_specs

ml-agents/mlagents/trainers/tests/simple_test_envs.py

         self.names = brain_names
         self.positions: Dict[str, List[float]] = {}
         self.step_count: Dict[str, float] = {}
-
-        # Concatenate the arguments for a consistent random seed
-        seed = (
-            brain_names,
-            step_size,
-            num_visual,
-            num_vector,
-            num_var_len,
-            vis_obs_size,
-            vec_obs_size,
-            var_len_obs_size,
-            action_sizes,
-        )
-        self.random = random.Random(str(seed))
-
+        self.random = random.Random(str(self.behavior_spec))
         self.goal: Dict[str, int] = {}
         self.action = {}
         self.rewards: Dict[str, float] = {}

ml-agents/mlagents/trainers/tests/test_buffer.py

             ]
         ),
     )
-
-    # Test padding
-    a = agent_2_buffer[ObsUtil.get_name_at(0)].get_batch(
-        batch_size=None, training_length=4, sequential=True
-    )
-    assert_array(
-        np.array(a),
-        np.array(
-            [
-                [0, 0, 0],
-                [0, 0, 0],
-                [0, 0, 0],
-                [201, 202, 203],
-                [211, 212, 213],
-                [221, 222, 223],
-                [231, 232, 233],
-                [241, 242, 243],
-                [251, 252, 253],
-                [261, 262, 263],
-                [271, 272, 273],
-                [281, 282, 283],
-            ]
-        ),
-    )
-    # Test group entries return Lists of Lists. Make sure to pad properly!
+    # Test group entries return Lists of Lists
-        batch_size=None, training_length=4, sequential=True
+        batch_size=2, training_length=1, sequential=True
-    for _group_entry in a[:3]:
-        assert len(_group_entry) == 0
-    for _group_entry in a[3:]:
+    for _group_entry in a:
         assert len(_group_entry) == 3
 
     agent_1_buffer.reset_agent()
     padded = c.padded_to_batch(pad_value=3)
     assert np.array_equal(padded[0], np.array([1, 1, 1, 1]))
     assert np.array_equal(padded[1], np.array([2, 2, 3, 3]))
-
-    # Make sure it doesn't fail when the field isn't a list
-    padded_a = a.padded_to_batch()
-    assert np.array_equal(padded_a, a)
 
 
 def fakerandint(values):

ml-agents/mlagents/trainers/tests/torch/test_hybrid.py

 
 
 @pytest.mark.check_environment_trains
-@pytest.mark.parametrize("num_visual,training_seed", [(1, 1336), (2, 1338)])
-def test_hybrid_visual_ppo(num_visual, training_seed):
+@pytest.mark.parametrize("num_visual", [1, 2])
+def test_hybrid_visual_ppo(num_visual):
-        PPO_TORCH_CONFIG.hyperparameters,
-        batch_size=64,
-        buffer_size=1024,
-        learning_rate=1e-4,
+        PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
-    config = attr.evolve(
-        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=8000
-    )
-    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=training_seed)
+    config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1336)
 
 
 @pytest.mark.check_environment_trains
         buffer_init_steps=0,
     )
     config = attr.evolve(
-        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=6000
+        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=2200
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+    check_environment_trains(
+        env, {BRAIN_NAME: config}, success_threshold=0.9, training_seed=1336
+    )
-@pytest.mark.parametrize("num_visual,training_seed", [(1, 1337), (2, 1338)])
-def test_hybrid_visual_sac(num_visual, training_seed):
+@pytest.mark.parametrize("num_visual", [1, 2])
+def test_hybrid_visual_sac(num_visual):
     env = SimpleEnvironment(
         [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
     )
     config = attr.evolve(
         SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=3000
     )
-    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=training_seed)
+    check_environment_trains(env, {BRAIN_NAME: config})
 
 
 @pytest.mark.check_environment_trains
     new_hyperparams = attr.evolve(
         SAC_TORCH_CONFIG.hyperparameters,
         batch_size=256,
-        learning_rate=3e-4,
+        learning_rate=1e-3,
         buffer_init_steps=1000,
         steps_per_update=2,
     )
         network_settings=new_networksettings,
-        max_steps=4000,
+        max_steps=3500,
     )
     check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1212)

ml-agents/mlagents/trainers/tests/torch/saver/test_saver.py

-from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
 import pytest
 from unittest import mock
 import os
 from mlagents.trainers.policy.torch_policy import TorchPolicy
 from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
-from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
-from mlagents.trainers.settings import TrainerSettings, PPOSettings, SACSettings
+from mlagents.trainers.settings import TrainerSettings
 from mlagents.trainers.tests import mock_brain as mb
 from mlagents.trainers.tests.torch.test_policy import create_policy_mock
 from mlagents.trainers.torch.utils import ModelUtils
     assert model_saver.policy is not None
 
 
-def test_load_save_policy(tmp_path):
+def test_load_save(tmp_path):
     path1 = os.path.join(tmp_path, "runid1")
     path2 = os.path.join(tmp_path, "runid2")
     trainer_params = TrainerSettings()
     assert policy3.get_current_step() == 0
 
 
-@pytest.mark.parametrize(
-    "optimizer",
-    [(TorchPPOOptimizer, PPOSettings), (TorchSACOptimizer, SACSettings)],
-    ids=["ppo", "sac"],
-)
-def test_load_save_optimizer(tmp_path, optimizer):
-    OptimizerClass, HyperparametersClass = optimizer
-
-    trainer_settings = TrainerSettings()
-    trainer_settings.hyperparameters = HyperparametersClass()
-    policy = create_policy_mock(trainer_settings, use_discrete=False)
-    optimizer = OptimizerClass(policy, trainer_settings)
-
-    # save at path 1
-    path1 = os.path.join(tmp_path, "runid1")
-    model_saver = TorchModelSaver(trainer_settings, path1)
-    model_saver.register(policy)
-    model_saver.register(optimizer)
-    model_saver.initialize_or_load()
-    policy.set_step(2000)
-    model_saver.save_checkpoint("MockBrain", 2000)
-
-    # create a new optimizer and policy
-    policy2 = create_policy_mock(trainer_settings, use_discrete=False)
-    optimizer2 = OptimizerClass(policy2, trainer_settings)
-
-    # load weights
-    model_saver2 = TorchModelSaver(trainer_settings, path1, load=True)
-    model_saver2.register(policy2)
-    model_saver2.register(optimizer2)
-    model_saver2.initialize_or_load()  # This is to load the optimizers
-
-    # Compare the two optimizers
-    _compare_two_optimizers(optimizer, optimizer2)
-
-
 # TorchPolicy.evalute() returns log_probs instead of all_log_probs like tf does.
 # resulting in indeterministic results for testing.
 # So here use sample_actions instead.
         ModelUtils.to_numpy(log_probs1.all_discrete_tensor),
         ModelUtils.to_numpy(log_probs2.all_discrete_tensor),
     )
-
-
-def _compare_two_optimizers(opt1: TorchOptimizer, opt2: TorchOptimizer) -> None:
-    trajectory = mb.make_fake_trajectory(
-        length=10,
-        observation_specs=opt1.policy.behavior_spec.observation_specs,
-        action_spec=opt1.policy.behavior_spec.action_spec,
-        max_step_complete=True,
-    )
-    with torch.no_grad():
-        _, opt1_val_out, _ = opt1.get_trajectory_value_estimates(
-            trajectory.to_agentbuffer(), trajectory.next_obs, done=False
-        )
-        _, opt2_val_out, _ = opt2.get_trajectory_value_estimates(
-            trajectory.to_agentbuffer(), trajectory.next_obs, done=False
-        )
-
-    for opt1_val, opt2_val in zip(opt1_val_out.values(), opt2_val_out.values()):
-        np.testing.assert_array_equal(opt1_val, opt2_val)
 
 
 @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])

ml-agents/mlagents/trainers/tests/torch/test_ppo.py

     run_out, final_value_out, all_memories = optimizer.get_trajectory_value_estimates(
         trajectory.to_agentbuffer(), trajectory.next_obs, done=False
     )
-    if rnn:
-        # Check that memories don't have a Torch gradient
-        for mem in optimizer.critic_memory_dict.values():
-            assert not mem.requires_grad
-
     for key, val in run_out.items():
         assert type(key) is str
         assert len(val) == 15

ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py

     )
     bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1500)
     reward_signals = {
-        RewardSignalType.GAIL: GAILSettings(
-            gamma=0.8, encoding_size=32, demo_path=demo_path
-        )
+        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
-    hyperparams = attr.evolve(PPO_TORCH_CONFIG.hyperparameters, learning_rate=1e-3)
+    hyperparams = attr.evolve(PPO_TORCH_CONFIG.hyperparameters, learning_rate=5e-3)
     config = attr.evolve(
         PPO_TORCH_CONFIG,
         reward_signals=reward_signals,

ml-agents/setup.py

         "pyyaml>=3.1.0",
         # Windows ver. of PyTorch doesn't work from PyPi. Installation:
         # https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Installation.md#windows-installing-pytorch
-        # Torch only working on python 3.9 for 1.8.0 and above. Details see:
-        # https://github.com/pytorch/pytorch/issues/50014
-        "torch>=1.8.0,<1.9.0;(platform_system!='Windows' and python_version>='3.9')",
-        "torch>=1.6.0,<1.9.0;(platform_system!='Windows' and python_version<'3.9')",
+        'torch>=1.6.0,<1.8.0;platform_system!="Windows"',
         "tensorboard>=1.15",
         # cattrs 1.1.0 dropped support for python 3.6, but 1.0.0 doesn't work for python 3.9
         # Since there's no version that supports both, we have to draw the line somwehere.

ml-agents-envs/README.md

 Install the `mlagents_envs` package with:
 
 ```sh
-python -m pip install mlagents_envs==0.24.1
+pip3 install mlagents_envs
 ```
 
 ## Usage & More Information

ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py

   name='mlagents_envs/communicator_objects/observation.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\x8f\x03\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x12\"\n\x1a\x63ompressed_channel_mapping\x18\x05 \x03(\x05\x12\x1c\n\x14\x64imension_properties\x18\x06 \x03(\x05\x12\x44\n\x10observation_type\x18\x07 \x01(\x0e\x32*.communicator_objects.ObservationTypeProto\x12\x0c\n\x04name\x18\x08 \x01(\t\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01*F\n\x14ObservationTypeProto\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x08\n\x04GOAL\x10\x01\x12\n\n\x06REWARD\x10\x02\x12\x0b\n\x07MESSAGE\x10\x03\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\x81\x03\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x12\"\n\x1a\x63ompressed_channel_mapping\x18\x05 \x03(\x05\x12\x1c\n\x14\x64imension_properties\x18\x06 \x03(\x05\x12\x44\n\x10observation_type\x18\x07 \x01(\x0e\x32*.communicator_objects.ObservationTypeProto\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01*F\n\x14ObservationTypeProto\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x08\n\x04GOAL\x10\x01\x12\n\n\x06REWARD\x10\x02\x12\x0b\n\x07MESSAGE\x10\x03\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 _COMPRESSIONTYPEPROTO = _descriptor.EnumDescriptor(
   ],
   containing_type=None,
   options=None,
-  serialized_start=480,
-  serialized_end=521,
+  serialized_start=466,
+  serialized_end=507,
 )
 _sym_db.RegisterEnumDescriptor(_COMPRESSIONTYPEPROTO)
 
   ],
   containing_type=None,
   options=None,
-  serialized_start=523,
-  serialized_end=593,
+  serialized_start=509,
+  serialized_end=579,
 )
 _sym_db.RegisterEnumDescriptor(_OBSERVATIONTYPEPROTO)
 
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=433,
-  serialized_end=458,
+  serialized_start=419,
+  serialized_end=444,
 )
 
 _OBSERVATIONPROTO = _descriptor.Descriptor(
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='name', full_name='communicator_objects.ObservationProto.name', index=7,
-      number=8, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
       index=0, containing_type=None, fields=[]),
   ],
   serialized_start=79,
-  serialized_end=478,
+  serialized_end=464,
 )
 
 _OBSERVATIONPROTO_FLOATDATA.containing_type = _OBSERVATIONPROTO

ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.pyi

     Iterable as typing___Iterable,
     List as typing___List,
     Optional as typing___Optional,
-    Text as typing___Text,
     Tuple as typing___Tuple,
     cast as typing___cast,
 )
     compressed_channel_mapping = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
     dimension_properties = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
     observation_type = ... # type: ObservationTypeProto
-    name = ... # type: typing___Text
 
     @property
     def float_data(self) -> ObservationProto.FloatData: ...
         compressed_channel_mapping : typing___Optional[typing___Iterable[builtin___int]] = None,
         dimension_properties : typing___Optional[typing___Iterable[builtin___int]] = None,
         observation_type : typing___Optional[ObservationTypeProto] = None,
-        name : typing___Optional[typing___Text] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> ObservationProto: ...
         def HasField(self, field_name: typing_extensions___Literal[u"compressed_data",u"float_data",u"observation_data"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",u"compressed_data",u"compression_type",u"dimension_properties",u"float_data",u"name",u"observation_data",u"observation_type",u"shape"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",u"compressed_data",u"compression_type",u"dimension_properties",u"float_data",u"observation_data",u"observation_type",u"shape"]) -> None: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",b"compressed_channel_mapping",u"compressed_data",b"compressed_data",u"compression_type",b"compression_type",u"dimension_properties",b"dimension_properties",u"float_data",b"float_data",u"name",b"name",u"observation_data",b"observation_data",u"observation_type",b"observation_type",u"shape",b"shape"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",b"compressed_channel_mapping",u"compressed_data",b"compressed_data",u"compression_type",b"compression_type",u"dimension_properties",b"dimension_properties",u"float_data",b"float_data",u"observation_data",b"observation_data",u"observation_type",b"observation_type",u"shape",b"shape"]) -> None: ...
     def WhichOneof(self, oneof_group: typing_extensions___Literal[u"observation_data",b"observation_data"]) -> typing_extensions___Literal["compressed_data","float_data"]: ...

ml-agents-envs/mlagents_envs/base_env.py

     dimension_property: Tuple[DimensionProperty, ...]
     observation_type: ObservationType
 
-    # Optional name. For observations coming from com.unity.ml-agents, this
-    # will be the ISensor name.
-    name: str
-
 
 class BehaviorSpec(NamedTuple):
     """

ml-agents-envs/mlagents_envs/rpc_utils.py

     for obs in agent_info.observations:
         observation_specs.append(
             ObservationSpec(
-                name=obs.name,
-                shape=tuple(obs.shape),
-                observation_type=ObservationType(obs.observation_type),
-                dimension_property=tuple(
-                    DimensionProperty(dim) for dim in obs.dimension_properties
-                )
+                tuple(obs.shape),
+                tuple(DimensionProperty(dim) for dim in obs.dimension_properties)
+                ObservationType(obs.observation_type),
             )
         )
 
     return img
 
 
-def _check_observations_match_spec(
-    obs_index: int,
-    observation_spec: ObservationSpec,
-    agent_info_list: Collection[AgentInfoProto],
-) -> None:
-    """
-    Check that all the observations match the expected size.
-    This gives a nicer error than a cryptic numpy error later.
-    """
-    expected_obs_shape = tuple(observation_spec.shape)
-    for agent_info in agent_info_list:
-        agent_obs_shape = tuple(agent_info.observations[obs_index].shape)
-        if expected_obs_shape != agent_obs_shape:
-            raise UnityObservationException(
-                f"Observation at index={obs_index} for agent with "
-                f"id={agent_info.id} didn't match the ObservationSpec. "
-                f"Expected shape {expected_obs_shape} but got {agent_obs_shape}."
-            )
-
-
 @timed
 def _observation_to_np_array(
     obs: ObservationProto, expected_shape: Optional[Iterable[int]] = None
 @timed
 def _process_maybe_compressed_observation(
     obs_index: int,
-    observation_spec: ObservationSpec,
+    shape: Tuple[int, int, int],
-    shape = cast(Tuple[int, int, int], observation_spec.shape)
-    try:
-        batched_visual = [
-            _observation_to_np_array(agent_obs.observations[obs_index], shape)
-            for agent_obs in agent_info_list
-        ]
-    except ValueError:
-        # Try to get a more useful error message
-        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
-        # If that didn't raise anything, raise the original error
-        raise
+    batched_visual = [
+        _observation_to_np_array(agent_obs.observations[obs_index], shape)
+        for agent_obs in agent_info_list
+    ]
     return np.array(batched_visual, dtype=np.float32)
 
 
 
 @timed
 def _process_rank_one_or_two_observation(
-    obs_index: int,
-    observation_spec: ObservationSpec,
-    agent_info_list: Collection[AgentInfoProto],
+    obs_index: int, shape: Tuple[int, ...], agent_info_list: Collection[AgentInfoProto]
-        return np.zeros((0,) + observation_spec.shape, dtype=np.float32)
-    try:
-        np_obs = np.array(
-            [
-                agent_obs.observations[obs_index].float_data.data
-                for agent_obs in agent_info_list
-            ],
-            dtype=np.float32,
-        ).reshape((len(agent_info_list),) + observation_spec.shape)
-    except ValueError:
-        # Try to get a more useful error message
-        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
-        # If that didn't raise anything, raise the original error
-        raise
+        return np.zeros((0,) + shape, dtype=np.float32)
+    np_obs = np.array(
+        [
+            agent_obs.observations[obs_index].float_data.data
+            for agent_obs in agent_info_list
+        ],
+        dtype=np.float32,
+    ).reshape((len(agent_info_list),) + shape)
     _raise_on_nan_and_inf(np_obs, "observations")
     return np_obs
 
     ]
     decision_obs_list: List[np.ndarray] = []
     terminal_obs_list: List[np.ndarray] = []
-    for obs_index, observation_spec in enumerate(behavior_spec.observation_specs):
-        is_visual = len(observation_spec.shape) == 3
+    for obs_index, observation_specs in enumerate(behavior_spec.observation_specs):
+        is_visual = len(observation_specs.shape) == 3
+            obs_shape = cast(Tuple[int, int, int], observation_specs.shape)
-                    obs_index, observation_spec, decision_agent_info_list
+                    obs_index, obs_shape, decision_agent_info_list
-                    obs_index, observation_spec, terminal_agent_info_list
+                    obs_index, obs_shape, terminal_agent_info_list
-                    obs_index, observation_spec, decision_agent_info_list
+                    obs_index, observation_specs.shape, decision_agent_info_list
-                    obs_index, observation_spec, terminal_agent_info_list
+                    obs_index, observation_specs.shape, terminal_agent_info_list
                 )
             )
     decision_rewards = np.array(

ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py

 def test_vector_observation():
     n_agents = 10
     shapes = [(3,), (4,)]
-    obs_specs = create_observation_specs_with_shapes(shapes)
-        arr = _process_rank_one_or_two_observation(
-            obs_index, obs_specs[obs_index], list_proto
-        )
+        arr = _process_rank_one_or_two_observation(obs_index, shape, list_proto)
-    shape = (128, 64, 3)
-    in_array_1 = np.random.rand(*shape)
+    in_array_1 = np.random.rand(128, 64, 3)
-    in_array_2 = np.random.rand(*shape)
+    in_array_2 = np.random.rand(128, 64, 3)
     in_array_2_mapping = [0, 1, 2]
     proto_obs_2 = generate_compressed_proto_obs_with_mapping(
         in_array_2, in_array_2_mapping
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 3), ap_list)
     assert list(arr.shape) == [2, 128, 64, 3]
     assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    shape = (128, 64, 1)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 1), ap_list)
     assert list(arr.shape) == [2, 128, 64, 1]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-    shape = (128, 64, 8)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 8), ap_list)
     assert list(arr.shape) == [1, 128, 64, 8]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-
-    shape = (128, 42, 3)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-
-        _process_maybe_compressed_observation(0, obs_spec, ap_list)
+        _process_maybe_compressed_observation(0, (128, 42, 3), ap_list)
 
 
 def test_batched_step_result_from_proto():
     assert decision_steps.obs[1].shape[1] == shapes[1][0]
     assert terminal_steps.obs[0].shape[1] == shapes[0][0]
     assert terminal_steps.obs[1].shape[1] == shapes[1][0]
-
-
-def test_mismatch_observations_raise_in_step_result_from_proto():
-    n_agents = 10
-    shapes = [(3,), (4,)]
-    spec = BehaviorSpec(
-        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
-    )
-    ap_list = generate_list_agent_proto(n_agents, shapes)
-    # Hack an observation to be larger, we should get an exception
-    ap_list[0].observations[0].shape[0] += 1
-    ap_list[0].observations[0].float_data.data.append(0.42)
-    with pytest.raises(UnityObservationException):
-        steps_from_proto(ap_list, spec)
 
 
 def test_action_masking_discrete():

protobuf-definitions/proto/mlagents_envs/communicator_objects/observation.proto

     repeated int32 compressed_channel_mapping = 5;
     repeated int32 dimension_properties = 6;
     ObservationTypeProto observation_type = 7;
-    // Optional name of the observation.
-    // This will be set to the ISensor name when writing,
-    // and read into the ObservationSpec in the low-level API
-    string name = 8;
 }

com.unity.ml-agents.extensions/Documentation~/Grid-Sensor.md

 
 An image can be thought of as a matrix of a predefined width (W) and a height (H) and each pixel can be thought of as simply an array of length 3 (in the case of RGB), `[Red, Green, Blue]` holding the different channel information of the color (channel) intensities at that pixel location. Thus an image is just a 3 dimensional matrix of size WxHx3. A Grid Observation can be thought of as a generalization of this setup where in place of a pixel there is a "cell" which is an array of length N representing different channel intensities at that cell position. From a Convolutional Neural Network point of view, the introduction of multiple channels in an "image" isn't a new concept. One such example is using an RGB-Depth image which is used in several robotics applications. The distinction of Grid Observations is what the data within the channels represents. Instead of limiting the channels to color intensities, the channels within a cell of a Grid Observation generalize to any data that can be represented by a single number (float or int).
 
-Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
+Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
 
 The Food Collector environment can be described as:
 * Set-up: A multi-agent environment where agents compete to collect food.

com.unity.ml-agents.extensions/Documentation~/Match3.md

 This implementation includes:
 
 * C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
-* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/docs/Learning-Environment-Examples.md#match-3).
+* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/docs/Learning-Environment-Examples.md#match-3).
 
 ### Feedback
 If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9).  If selected, we will provide gift cards as a token of appreciation.

com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md

 recommended ways to install the package:
 
 ### Local Installation
-[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
-[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#advanced-local-installation-for-development-1)
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
+[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Installation.md#advanced-local-installation-for-development-1)
-![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/images/unity_package_manager_git_url.png)
-
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/images/unity_package_manager_git_url.png)
-git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14
+ git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions
-"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14",
+"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions",
-See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
-may take several minutes to resolve the packages the first time that you add it.
+See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information.
 
 
 ## Requirements
     - No way to customize the action space of the `InputActuatorComponent`
 
 ## Need Help?
-The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/README.md) contains links for contacting the team or getting support.
+The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/README.md) contains links for contacting the team or getting support.

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             Assert.IsTrue(Mathf.Approximately(1f, (float)m_Action.ReadValue<double>()));
         }
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<int>();
             Assert.IsTrue(val == 1);
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new int[1]));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<Vector2>();
             Assert.IsTrue(Mathf.Approximately(0f, val.x));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new float[2]), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs

 using Unity.MLAgents.Policies;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.LowLevel;
-        public bool eventWritten;
+        public bool eventQueued;
         public bool writtenToHeuristic;
 
         public ActionSpec GetActionSpecForInputAction(InputAction action)
 
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            eventWritten = true;
+            eventQueued = true;
         }
 
         public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
 
         public void Reset()
         {
-            eventWritten = false;
+            eventQueued = false;
             writtenToHeuristic = false;
         }
     }
             m_BehaviorParameters = go.AddComponent<BehaviorParameters>();
             var action = new InputAction("action");
             m_Adaptor = new TestAdaptor();
-            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor, new InputActuatorEventContext(1, InputSystem.AddDevice<Gamepad>()));
+            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor);
         }
 
         [Test]
             m_Actuator.OnActionReceived(new ActionBuffers());
             m_Actuator.Heuristic(new ActionBuffers());
-            Assert.IsFalse(m_Adaptor.eventWritten);
+            Assert.IsFalse(m_Adaptor.eventQueued);
-            Assert.IsFalse(m_Adaptor.eventWritten);
+            Assert.IsFalse(m_Adaptor.eventQueued);
-            Assert.IsTrue(m_Adaptor.eventWritten);
+            Assert.IsTrue(m_Adaptor.eventQueued);
             m_Adaptor.Reset();
 
             Assert.AreEqual(m_Actuator.Name, "InputActionActuator-action");

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs

 
             var device = InputSystem.AddDevice("TestLayout");
 
-            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device, new InputActuatorEventContext());
+            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device);
             Assert.IsTrue(actuators.Length == 2);
             Assert.IsTrue(actuators[0].ActionSpec.Equals(ActionSpec.MakeContinuous(2)));
             Assert.IsTrue(actuators[1].ActionSpec.NumDiscreteActions == 1);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0-preview.3",
+            "expression": "1.1.0",
             "define": "MLA_INPUT_TESTS"
         }
     ],

com.unity.ml-agents.extensions/package.json

 {
   "name": "com.unity.ml-agents.extensions",
   "displayName": "ML Agents Extensions",
-  "version": "0.2.0-preview",
+  "version": "0.1.0-preview",
-    "com.unity.ml-agents": "1.8.1-preview"
+    "com.unity.ml-agents": "1.8.0-preview"
   }
 }

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
         }
 
         /// TODO again this might need to be more nuanced for things like continuous buttons.
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            ((ButtonControl)control).WriteValueIntoEvent((float)val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, (byte)val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            ((DoubleControl)control).WriteValueIntoEvent((double)val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control,(double)val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs

             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs

             return ActionSpec.MakeDiscrete(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action,
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action,
             InputControl control,
             ActionSpec actionSpec,
             in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(new Vector2(x, y), eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, new Vector2(x, y));
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs

 using System;
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
 {
         /// <summary>
         /// Translates data from the <see cref="ActionBuffers"/> object to the <see cref="InputSystem"/>.
         /// </summary>
-        /// <param name="eventPtr">The Event pointer to write to.</param>
-        void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
+        void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
 
         /// <summary>
         /// Writes data from the <paramref name="action"/> to the <paramref name="actionBuffers"/>.

com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0-preview.3",
+            "expression": "1.1.0-preview",
-}
+}

com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs

         readonly BehaviorParameters m_BehaviorParameters;
         readonly InputAction m_Action;
         readonly IRLActionInputAdaptor m_InputAdaptor;
-        InputActuatorEventContext m_InputActuatorEventContext;
         InputDevice m_Device;
         InputControl m_Control;
 
         ///     via the <see cref="IRLActionInputAdaptor"/>.</param>
         /// <param name="adaptor">The <see cref="IRLActionInputAdaptor"/> that will convert data between ML-Agents
         ///     and the <see cref="InputSystem"/>.</param>
-        /// <param name="inputActuatorEventContext">The object that will provide the event ptr to write to.</param>
-                                   IRLActionInputAdaptor adaptor,
-                                   InputActuatorEventContext inputActuatorEventContext)
+                                   IRLActionInputAdaptor adaptor)
-            m_InputActuatorEventContext = inputActuatorEventContext;
             ActionSpec = adaptor.GetActionSpecForInputAction(m_Action);
             m_Device = inputDevice;
             m_Control = m_Device?.GetChildControl(m_Action.name);
             Profiler.BeginSample("InputActionActuator.OnActionReceived");
             if (!m_BehaviorParameters.IsInHeuristicMode())
             {
-                using (m_InputActuatorEventContext.GetEventForFrame(out var eventPtr))
-                {
-                    m_InputAdaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, ActionSpec, actionBuffers);
-                }
-
+                m_InputAdaptor.QueueInputEventForAction(m_Action, m_Control, ActionSpec, actionBuffers);
             }
             Profiler.EndSample();
         }

com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using System;
 using System.Collections.Generic;
-using Unity.Collections;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Policies;
 using UnityEngine;
-using UnityEngine.InputSystem.LowLevel;
 using UnityEngine.InputSystem.Layouts;
 using UnityEngine.InputSystem.Utilities;
 #if UNITY_EDITOR
             get
             {
 #if UNITY_EDITOR
-                if (!EditorApplication.isPlaying && m_ActionSpec.NumContinuousActions == 0
-                    && m_ActionSpec.BranchSizes == null
-                    || m_ActionSpec.BranchSizes.Length == 0)
-                {
-                    FindNeededComponents();
-                    var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap),
-                        m_BehaviorParameters,
-                        null,
-                        InputActuatorEventContext.s_EditorContext);
-                    m_ActionSpec = CombineActuatorActionSpecs(actuators);
-
-                }
+                FindNeededComponents();
+                var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap), m_BehaviorParameters, null);
+                m_ActionSpec = CombineActuatorActionSpecs(actuators);
 #endif
                 return m_ActionSpec;
             }
             RegisterLayoutBuilder(inputActionMap, m_LayoutName);
             m_Device = InputSystem.AddDevice(m_LayoutName);
 
-            var context = new InputActuatorEventContext(inputActionMap.actions.Count, m_Device);
-            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device, context);
+            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device);
 
             UpdateDeviceBinding(m_BehaviorParameters.IsInHeuristicMode());
             inputActionMap.Enable();
 
         internal static IActuator[] CreateActuatorsFromMap(InputActionMap inputActionMap,
             BehaviorParameters behaviorParameters,
-            InputDevice inputDevice,
-            InputActuatorEventContext context)
+            InputDevice inputDevice)
         {
             var actuators = new IActuator[inputActionMap.actions.Count];
             for (var i = 0; i < inputActionMap.actions.Count; i++)
                 var adaptor = (IRLActionInputAdaptor)Activator.CreateInstance(controlTypeToAdaptorType[actionLayout.type]);
-                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor, context);
+                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor);
 
                 // Reasonably, the input system starts adding numbers after the first none numbered name
                 // is added.  So for device ID of 0, we use the empty string in the path.
                     action.processors,
                     mlAgentsControlSchemeName);
-                action.bindingMask = InputBinding.MaskByGroup(mlAgentsControlSchemeName);
             }
             return actuators;
         }
             m_PlayerInput = null;
             m_BehaviorParameters = null;
             m_Device = null;
-        }
-
-        int m_ActuatorsWrittenToEvent;
-        NativeArray<byte> m_InputBufferForFrame;
-        InputEventPtr m_InputEventPtrForFrame;
-        public InputEventPtr GetEventForFrame()
-        {
-#if UNITY_EDITOR
-            if (!EditorApplication.isPlaying)
-            {
-                return new InputEventPtr();
-            }
-#endif
-            if (m_ActuatorsWrittenToEvent % m_Actuators.Length == 0 || !m_InputEventPtrForFrame.valid)
-            {
-                m_ActuatorsWrittenToEvent = 0;
-                m_InputEventPtrForFrame = new InputEventPtr();
-                m_InputBufferForFrame = StateEvent.From(m_Device, out m_InputEventPtrForFrame);
-            }
-
-            return m_InputEventPtrForFrame;
-        }
-
-        public void EventProcessedInFrame()
-        {
-#if UNITY_EDITOR
-            if (!EditorApplication.isPlaying)
-            {
-                return;
-            }
-#endif
-            m_ActuatorsWrittenToEvent++;
-            if (m_ActuatorsWrittenToEvent == m_Actuators.Length && m_InputEventPtrForFrame.valid)
-            {
-                InputSystem.QueueEvent(m_InputEventPtrForFrame);
-                m_InputBufferForFrame.Dispose();
-            }
         }
     }
 }

Project/Project.sln.DotSettings

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Project/ProjectSettings/TagManager.asset

   - purpleAgent
   - purpleGoal
   - tile
+  - zombie
+  - blockLarge
+  - blockVeryLarge
+  - blockSmall
+  - door
+  - switch
+  - key
+  - lock
+  - dragon
   layers:
   - Default
   - TransparentFX

Project/Packages/manifest.json

   "dependencies": {
     "com.unity.ads": "2.0.8",
     "com.unity.analytics": "3.2.3",
+    "com.unity.barracuda": "1.2.1-preview",
     "com.unity.collab-proxy": "1.2.15",
     "com.unity.ml-agents": "file:../../com.unity.ml-agents",
     "com.unity.ml-agents.extensions": "file:../../com.unity.ml-agents.extensions",
     "com.unity.modules.video": "1.0.0",
     "com.unity.modules.vr": "1.0.0",
     "com.unity.modules.wind": "1.0.0",
-    "com.unity.modules.xr": "1.0.0",
-    "com.unity.nuget.newtonsoft-json": "2.0.0"
+    "com.unity.modules.xr": "1.0.0"
   },
   "testables": [
     "com.unity.ml-agents",

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Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class WormAgent : Agent
 {
+    //The type of agent behavior we want to use.
+    //This setting will determine how the agent is set up during initialization.
+    public enum WormAgentBehaviorType
+    {
+        WormDynamic,
+        WormStatic
+    }
+
+    [Tooltip(
+        "Dynamic - The agent will run towards a target that changes position.\n\n" +
+        "Static - The agent will run towards a static target. "
+    )]
+    public WormAgentBehaviorType typeOfWorm;
+
-    [Header("Target Prefabs")] public Transform TargetPrefab; //Target prefab to use in Dynamic envs
+    //Brains
+    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
+    [Header("NN Models")] public NNModel wormDyModel;
+    public NNModel wormStModel;
+
+    [Header("Target Prefabs")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
+    public Transform staticTargetPrefab; //Target prefab to use in Static envs
     private Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] public Transform bodySegment0;
 
     public override void Initialize()
     {
-        SpawnTarget(TargetPrefab, transform.position); //spawn target
+        SetAgentType();
+
 
         m_StartingPos = bodySegment0.position;
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
     void SpawnTarget(Transform prefab, Vector3 pos)
     {
         m_Target = Instantiate(prefab, pos, Quaternion.identity, transform);
+    }
+
+    /// <summary>
+    /// Set up the agent based on the type
+    /// </summary>
+    void SetAgentType()
+    {
+        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
+        switch (typeOfWorm)
+        {
+            case WormAgentBehaviorType.WormDynamic:
+                {
+                    behaviorParams.BehaviorName = "WormDynamic"; //set behavior name
+                    if (wormDyModel)
+                        behaviorParams.Model = wormDyModel; //assign the brain
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case WormAgentBehaviorType.WormStatic:
+                {
+                    behaviorParams.BehaviorName = "WormStatic"; //set behavior name
+                    if (wormStModel)
+                        behaviorParams.Model = wormStModel; //assign the brain
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+        }
     }
 
     /// <summary>

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Project/Assets/ML-Agents/Examples/Crawler/Scenes/CrawlerDynamicVariableSpeed.unity

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Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class CrawlerAgent : Agent
 {
+    //The type of crawler behavior we want to use.
+    //This setting will determine how the agent is set up during initialization.
+    public enum CrawlerAgentBehaviorType
+    {
+        CrawlerDynamic,
+        CrawlerDynamicVariableSpeed,
+        CrawlerStatic,
+        CrawlerStaticVariableSpeed,
+        CrawlerDynamicVariableSpeedCustomTarget
+    }
+
+    [Tooltip(
+        "VariableSpeed - The agent will sample random speed magnitudes while training.\n" +
+        "Dynamic - The agent will run towards a target that changes position.\n" +
+        "Static - The agent will run towards a static target. "
+    )]
+    public CrawlerAgentBehaviorType typeOfCrawler;
+
+    //Crawler Brains
+    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
+    [Header("NN Models")] public NNModel crawlerDyModel;
+    public NNModel crawlerDyVSModel;
+    public NNModel crawlerStModel;
+    public NNModel crawlerStVSModel;
 
     [Header("Walk Speed")]
     [Range(0.1f, m_maxWalkingSpeed)]
         set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
     }
 
+    //Should the agent sample a new goal velocity each episode?
+    //If true, TargetWalkingSpeed will be randomly set between 0.1 and m_maxWalkingSpeed in OnEpisodeBegin()
+    //If false, the goal velocity will be m_maxWalkingSpeed
+    private bool m_RandomizeWalkSpeedEachEpisode;
+
-    [Header("Target To Walk Towards")]
-    public Transform TargetPrefab; //Target prefab to use in Dynamic envs
-    private Transform m_Target; //Target the agent will walk towards during training.
+    [Header("Target To Walk Towards")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
+    public Transform staticTargetPrefab; //Target prefab to use in Static envs
+    public Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] [Space(10)] public Transform body;
     public Transform leg0Upper;
 
     public override void Initialize()
     {
-        SpawnTarget(TargetPrefab, transform.position); //spawn target
+        SetAgentType();
 
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
         m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
     }
 
     /// <summary>
+    /// Set up the agent based on the typeOfCrawler
+    /// </summary>
+    void SetAgentType()
+    {
+        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
+        switch (typeOfCrawler)
+        {
+            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeedCustomTarget:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
+                    if (crawlerDyVSModel)
+                        behaviorParams.Model = crawlerDyVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerDynamic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamic"; //set behavior name
+                    if (crawlerDyModel)
+                        behaviorParams.Model = crawlerDyModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
+                    if (crawlerDyVSModel)
+                        behaviorParams.Model = crawlerDyVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStatic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStatic"; //set behavior name
+                    if (crawlerStModel)
+                        behaviorParams.Model = crawlerStModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStaticVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStaticVariableSpeed"; //set behavior name
+                    if (crawlerStVSModel)
+                        behaviorParams.Model = crawlerStVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+        }
+    }
+
+    /// <summary>
     /// Loop over body parts and reset them to initial conditions.
     /// </summary>
     public override void OnEpisodeBegin()
         UpdateOrientationObjects();
 
         //Set our goal walking speed
-        TargetWalkingSpeed = Random.Range(0.1f, m_maxWalkingSpeed);
+        TargetWalkingSpeed =
+            m_RandomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : TargetWalkingSpeed;
     }
 
     /// <summary>

Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CrawlerBase.prefab

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Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs

 
     int m_NumberOfTilesToSpawn;
     int m_MaxNumberOfTiles;
+    PushBlockSettings m_PushBlockSettings;
     Rigidbody m_AgentRb;
 
     // The BufferSensorComponent is the Sensor that allows the Agent to observe
         m_MaxNumberOfTiles = k_HighestTileValue;
         m_ResetParams = Academy.Instance.EnvironmentParameters;
         m_BufferSensor = GetComponent<BufferSensorComponent>();
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
         m_AgentRb = GetComponent<Rigidbody>();
         m_StartingPos = transform.position;
     }
         }
 
         transform.Rotate(rotateDir, Time.deltaTime * 200f);
-        m_AgentRb.AddForce(dirToGo * 2, ForceMode.VelocityChange);
+        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
 
     }
 

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com.unity.ml-agents/.gitignore

 /Assets/Plugins*
 /Assets/Demonstrations*
 /csharp_timers.json
-/Samples/
-/Samples.meta
-*.api.meta
+*.api.meta

com.unity.ml-agents/Documentation~/com.unity.ml-agents.md

 [unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
 [unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
 [package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
-[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Installation.md
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Installation.md
-[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents.extensions
+[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents.extensions

com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs

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+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser, new[]{ "Shape", "CompressionType", "CompressedData", "FloatData", "CompressedChannelMapping", "DimensionProperties", "ObservationType" }, new[]{ "ObservationData" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData.Parser, new[]{ "Data" }, null, null, null)})
           }));
     }
     #endregion
       compressedChannelMapping_ = other.compressedChannelMapping_.Clone();
       dimensionProperties_ = other.dimensionProperties_.Clone();
       observationType_ = other.observationType_;
-      name_ = other.name_;
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           CompressedData = other.CompressedData;
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     }
 
-    /// <summary>Field number for the "name" field.</summary>
-    public const int NameFieldNumber = 8;
-    private string name_ = "";
-    /// <summary>
-    /// Optional name of the observation.
-    /// This will be set to the ISensor name when writing,
-    /// and read into the ObservationSpec in the low-level API
-    /// </summary>
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public string Name {
-      get { return name_; }
-      set {
-        name_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
-      }
-    }
-
     private object observationData_;
     /// <summary>Enum of possible cases for the "observation_data" oneof.</summary>
     public enum ObservationDataOneofCase {
       if(!compressedChannelMapping_.Equals(other.compressedChannelMapping_)) return false;
       if(!dimensionProperties_.Equals(other.dimensionProperties_)) return false;
       if (ObservationType != other.ObservationType) return false;
-      if (Name != other.Name) return false;
       if (ObservationDataCase != other.ObservationDataCase) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
       hash ^= compressedChannelMapping_.GetHashCode();
       hash ^= dimensionProperties_.GetHashCode();
       if (ObservationType != 0) hash ^= ObservationType.GetHashCode();
-      if (Name.Length != 0) hash ^= Name.GetHashCode();
       hash ^= (int) observationDataCase_;
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
         output.WriteRawTag(56);
         output.WriteEnum((int) ObservationType);
       }
-      if (Name.Length != 0) {
-        output.WriteRawTag(66);
-        output.WriteString(Name);
-      }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
       if (ObservationType != 0) {
         size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ObservationType);
       }
-      if (Name.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(Name);
-      }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
       dimensionProperties_.Add(other.dimensionProperties_);
       if (other.ObservationType != 0) {
         ObservationType = other.ObservationType;
-      }
-      if (other.Name.Length != 0) {
-        Name = other.Name;
       }
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           }
           case 56: {
             observationType_ = (global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto) input.ReadEnum();
-            break;
-          }
-          case 66: {
-            Name = input.ReadString();
             break;
           }
         }

com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs.meta

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+guid: 5661ffdb6c7704e84bc785572dcd5bd1
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   serializedVersion: 2