ml-agents/docs/Migrating.md

Upgrading

⚠️ Warning ⚠️

The C# editor code and python trainer code are not compatible between releases. This means that if you upgrade one, you must upgrade the other as well. If you experience new errors or unable to connect to training after updating, please double-check that the versions are in the same. The versions can be found in

• Academy.k_ApiVersion in Academy.cs (example)
• UnityEnvironment.API_VERSION in environment.py (example)

Migrating

Migrating from 0.14 to latest

Important changes

• The Agent.CollectObservations() virtual method now takes as input a VectorSensor sensor as argument. The Agent.AddVectorObs() methods were removed.
• The Monitor class has been moved to the Examples Project. (It was prone to errors during testing)
• The MLAgents.Sensor namespace has been removed. All sensors now belong to the MLAgents namespace.
• The SetActionMask method must now be called on the optional ActionMasker argument of the CollectObservations method. (We now consider an action mask as a type of observation)
• The method GetStepCount() on the Agent class has been replaced with the property getter StepCount

Steps to Migrate

• Replace your Agent's implementation of CollectObservations() with CollectObservations(VectorSensor sensor). In addition, replace all calls to AddVectorObs() with sensor.AddObservation() or sensor.AddOneHotObservation() on the VectorSensor passed as argument.
• Replace your calls to SetActionMask on your Agent to ActionMasker.SetActionMask in CollectObservations
• Re-import all of your *.NN files to work with the updated Barracuda package.
• Replace all calls to Agent.GetStepCount() with Agent.StepCount

Migrating from 0.13 to 0.14

Important changes

• The UnitySDK folder has been split into a Unity Package (com.unity.ml-agents) and an examples project (Project). Please follow the Installation Guide to get up and running with this new repo structure.
• Several changes were made to how agents are reset and marked as done:
  • Calling Done() on the Agent will now reset it immediately and call the AgentReset virtual method. (This is to simplify the previous logic in which the Agent had to wait for the next EnvironmentStep to reset)
  • The "Reset on Done" setting in AgentParameters was removed; this is now effectively always true. AgentOnDone virtual method on the Agent has been removed.
• The Decision Period and On Demand decision checkbox have been removed from the Agent. On demand decision is now the default (calling RequestDecision on the Agent manually.)
• The Academy class was changed to a singleton, and its virtual methods were removed.
• Trainer steps are now counted per-Agent, not per-environment as in previous versions. For instance, if you have 10 Agents in the scene, 20 environment steps now corresponds to 200 steps as printed in the terminal and in Tensorboard.
• Curriculum config files are now YAML formatted and all curricula for a training run are combined into a single file.
• The --num-runs command-line option has been removed from mlagents-learn.
• Several fields on the Agent were removed or made private in order to simplify the interface.
  • The agentParameters field of the Agent has been removed. (Contained only maxStep information)
  • maxStep is now a public field on the Agent. (Was moved from agentParameters)
  • The Info field of the Agent has been made private. (Was only used internally and not meant to be modified outside of the Agent)
  • The GetReward() method on the Agent has been removed. (It was being confused with GetCumulativeReward())
  • The AgentAction struct no longer contains a value field. (Value estimates were not set during inference)
  • The GetValueEstimate() method on the Agent has been removed.
  • The UpdateValueAction() method on the Agent has been removed.
• The deprecated RayPerception3D and RayPerception2D classes were removed, and the legacyHitFractionBehavior argument was removed from RayPerceptionSensor.PerceiveStatic().
• RayPerceptionSensor was inconsistent in how it handle scale on the Agent's transform. It now scales the ray length and sphere size for casting as the transform's scale changes.

Steps to Migrate

• Follow the instructions on how to install the com.unity.ml-agents package into your project in the Installation Guide.
• If your Agent implemented AgentOnDone and did not have the checkbox Reset On Done checked in the inspector, you must call the code that was in AgentOnDone manually.
• If you give your Agent a reward or penalty at the end of an episode (e.g. for reaching a goal or falling off of a platform), make sure you call AddReward() or SetReward() before calling Done(). Previously, the order didn't matter.
• If you were not using On Demand Decision for your Agent, you must add a DecisionRequester component to your Agent GameObject and set its Decision Period field to the old Decision Period of the Agent.
• If you have a class that inherits from Academy:
  • If the class didn't override any of the virtual methods and didn't store any additional data, you can just remove the old script from the scene.
  • If the class had additional data, create a new MonoBehaviour and store the data in the new MonoBehaviour instead.
  • If the class overrode the virtual methods, create a new MonoBehaviour and move the logic to it:
    • Move the InitializeAcademy code to MonoBehaviour.OnAwake
    • Move the AcademyStep code to MonoBehaviour.FixedUpdate
    • Move the OnDestroy code to MonoBehaviour.OnDestroy.
    • Move the AcademyReset code to a new method and add it to the Academy.OnEnvironmentReset action.
• Multiply max_steps and summary_freq in your trainer_config.yaml by the number of Agents in the scene.
• Combine curriculum configs into a single file. See the WallJump curricula for an example of the new curriculum config format. A tool like https://www.json2yaml.com may be useful to help with the conversion.
• If you have a model trained which uses RayPerceptionSensor and has non-1.0 scale in the Agent's transform, it must be retrained.

Migrating from ML-Agents toolkit v0.12.0 to v0.13.0

Important changes

• The low level Python API has changed. You can look at the document Low Level Python API documentation for more information. This should only affect you if you're writing a custom trainer; if you use mlagents-learn for training, this should be a transparent change.
  • reset() on the Low-Level Python API no longer takes a train_mode argument. To modify the performance/speed of the engine, you must use an EngineConfigurationChannel
  • reset() on the Low-Level Python API no longer takes a config argument. UnityEnvironment no longer has a reset_parameters field. To modify float properties in the environment, you must use a FloatPropertiesChannel. For more information, refer to the Low Level Python API documentation
• CustomResetParameters are now removed.
• The Academy no longer has a Training Configuration nor Inference Configuration field in the inspector. To modify the configuration from the Low-Level Python API, use an EngineConfigurationChannel. To modify it during training, use the new command line arguments --width, --height, --quality-level, --time-scale and --target-frame-rate in mlagents-learn.
• The Academy no longer has a Default Reset Parameters field in the inspector. The Academy class no longer has a ResetParameters. To access shared float properties with Python, use the new FloatProperties field on the Academy.
• Offline Behavioral Cloning has been removed. To learn from demonstrations, use the GAIL and Behavioral Cloning features with either PPO or SAC. See Imitation Learning for more information.
• mlagents.envs was renamed to mlagents_envs. The previous repo layout depended on PEP420, which caused problems with some of our tooling such as mypy and pylint.
• The official version of Unity ML-Agents supports is now 2018.4 LTS. If you run into issues, please consider deleting your library folder and reponening your projects. You will need to install the Barracuda package into your project in order to ML-Agents to compile correctly.

Steps to Migrate

• If you had a custom Training Configuration in the Academy inspector, you will need to pass your custom configuration at every training run using the new command line arguments --width, --height, --quality-level, --time-scale and --target-frame-rate.
• If you were using --slow in mlagents-learn, you will need to pass your old Inference Configuration of the Academy inspector with the new command line arguments --width, --height, --quality-level, --time-scale and --target-frame-rate instead.
• Any imports from mlagents.envs should be replaced with mlagents_envs.

Migrating from ML-Agents toolkit v0.11.0 to v0.12.0

Important Changes

• Text actions and observations, and custom action and observation protos have been removed.
• RayPerception3D and RayPerception2D are marked deprecated, and will be removed in a future release. They can be replaced by RayPerceptionSensorComponent3D and RayPerceptionSensorComponent2D.
• The Use Heuristic checkbox in Behavior Parameters has been replaced with a Behavior Type dropdown menu. This has the following options:
  • Default corresponds to the previous unchecked behavior, meaning that Agents will train if they connect to a python trainer, otherwise they will perform inference.
  • Heuristic Only means the Agent will always use the Heuristic() method. This corresponds to having "Use Heuristic" selected in 0.11.0.
  • Inference Only means the Agent will always perform inference.
• Barracuda was upgraded to 0.3.2, and it is now installed via the Unity Package Manager.

Steps to Migrate

• We fixed a bug in RayPerception3d.Perceive() that was causing the endOffset to be used incorrectly. However this may produce different behavior from previous versions if you use a non-zero startOffset. To reproduce the old behavior, you should increase the the value of endOffset by startOffset. You can verify your raycasts are performing as expected in scene view using the debug rays.
• If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D (and similarly for 2D). The settings, such as ray angles and detectable tags, are configured on the component now. RayPerception3D would contribute (# of rays) * (# of tags + 2) to the State Size in Behavior Parameters, but this is no longer necessary, so you should reduce the State Size by this amount. Making this change will require retraining your model, since the observations that RayPerceptionSensorComponent3D produces are different from the old behavior.
• If you see messages such as The type or namespace 'Barracuda' could not be found or The type or namespace 'Google' could not be found, you will need to install the Barracuda preview package.

Migrating from ML-Agents toolkit v0.10 to v0.11.0

Important Changes

• The definition of the gRPC service has changed.
• The online BC training feature has been removed.
• The BroadcastHub has been deprecated. If there is a training Python process, all LearningBrains in the scene will automatically be trained. If there is no Python process, inference will be used.
• The Brain ScriptableObjects have been deprecated. The Brain Parameters are now on the Agent and are referred to as Behavior Parameters. Make sure the Behavior Parameters is attached to the Agent GameObject.
• To use a heuristic behavior, implement the Heuristic() method in the Agent class and check the use heuristic checkbox in the Behavior Parameters.
• Several changes were made to the setup for visual observations (i.e. using Cameras or RenderTextures):
  • Camera resolutions are no longer stored in the Brain Parameters.
  • AgentParameters no longer stores lists of Cameras and RenderTextures
  • To add visual observations to an Agent, you must now attach a CameraSensorComponent or RenderTextureComponent to the agent. The corresponding Camera or RenderTexture can be added to these in the editor, and the resolution and color/grayscale is configured on the component itself.

Steps to Migrate

• In order to be able to train, make sure both your ML-Agents Python package and UnitySDK code come from the v0.11 release. Training will not work, for example, if you update the ML-Agents Python package, and only update the API Version in UnitySDK.
• If your Agents used visual observations, you must add a CameraSensorComponent corresponding to each old Camera in the Agent's camera list (and similarly for RenderTextures).
• Since Brain ScriptableObjects have been removed, you will need to delete all the Brain ScriptableObjects from your Assets folder. Then, add a Behavior Parameters component to each Agent GameObject. You will then need to complete the fields on the new Behavior Parameters component with the BrainParameters of the old Brain.

Migrating from ML-Agents toolkit v0.9 to v0.10

Important Changes

• We have updated the C# code in our repository to be in line with Unity Coding Conventions. This has changed the name of some public facing classes and enums.
• The example environments have been updated. If you were using these environments to benchmark your training, please note that the resulting rewards may be slightly different in v0.10.

Steps to Migrate

• UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs and its class Communicator have been renamed to UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs and ICommunicator respectively.
• The SpaceType Enums discrete, and continuous have been renamed to Discrete and Continuous.
• We have removed the Done call as well as the capacity to set Max Steps on the Academy. Therefore an AcademyReset will never be triggered from C# (only from Python). If you want to reset the simulation after a fixed number of steps, or when an event in the simulation occurs, we recommend looking at our multi-agent example environments (such as FoodCollector). In our examples, groups of Agents can be reset through an "Area" that can reset groups of Agents.
• The import for mlagents.envs.UnityEnvironment was removed. If you are using the Python API, change from mlagents_envs import UnityEnvironment to from mlagents_envs.environment import UnityEnvironment.

Migrating from ML-Agents toolkit v0.8 to v0.9

Important Changes

• We have changed the way reward signals (including Curiosity) are defined in the trainer_config.yaml.
• When using multiple environments, every "step" is recorded in TensorBoard.
• The steps in the command line console corresponds to a single step of a single environment. Previously, each step corresponded to one step for all environments (i.e., num_envs steps).

Steps to Migrate

• If you were overriding any of these following parameters in your config file, remove them from the top-level config and follow the steps below:
  • gamma: Define a new extrinsic reward signal and set it's gamma to your new gamma.
  • use_curiosity, curiosity_strength, curiosity_enc_size: Define a curiosity reward signal and set its strength to curiosity_strength, and encoding_size to curiosity_enc_size. Give it the same gamma as your extrinsic signal to mimic previous behavior. See Reward Signals for more information on defining reward signals.
• TensorBoards generated when running multiple environments in v0.8 are not comparable to those generated in v0.9 in terms of step count. Multiply your v0.8 step count by num_envs for an approximate comparison. You may need to change max_steps in your config as appropriate as well.

Migrating from ML-Agents toolkit v0.7 to v0.8

Important Changes

• We have split the Python packages into two separate packages ml-agents and ml-agents-envs.
• --worker-id option of learn.py has been removed, use --base-port instead if you'd like to run multiple instances of learn.py.

Steps to Migrate

• If you are installing via PyPI, there is no change.
• If you intend to make modifications to ml-agents or ml-agents-envs please check the Installing for Development in the Installation documentation.

Migrating from ML-Agents toolkit v0.6 to v0.7

Important Changes

• We no longer support TFS and are now using the Unity Inference Engine

Steps to Migrate

• Make sure to remove the ENABLE_TENSORFLOW flag in your Unity Project settings

Migrating from ML-Agents toolkit v0.5 to v0.6

Important Changes

• Brains are now Scriptable Objects instead of MonoBehaviors.

• You can no longer modify the type of a Brain. If you want to switch between PlayerBrain and LearningBrain for multiple agents, you will need to assign a new Brain to each agent separately. Note: You can pass the same Brain to multiple agents in a scene by leveraging Unity's prefab system or look for all the agents in a scene using the search bar of the Hierarchy window with the word Agent.

• We replaced the Internal and External Brain with Learning Brain. When you need to train a model, you need to drag it into the Broadcast Hub inside the Academy and check the Control checkbox.

• We removed the Broadcast checkbox of the Brain, to use the broadcast functionality, you need to drag the Brain into the Broadcast Hub.

• When training multiple Brains at the same time, each model is now stored into a separate model file rather than in the same file under different graph scopes.

• The Learning Brain graph scope, placeholder names, output names and custom placeholders can no longer be modified.

Steps to Migrate

• To update a scene from v0.5 to v0.6, you must:
  • Remove the Brain GameObjects in the scene. (Delete all of the Brain GameObjects under Academy in the scene.)
  • Create new Brain Scriptable Objects using Assets -> Create -> ML-Agents for each type of the Brain you plan to use, and put the created files under a folder called Brains within your project.
  • Edit their Brain Parameters to be the same as the parameters used in the Brain GameObjects.
  • Agents have a Brain field in the Inspector, you need to drag the appropriate Brain ScriptableObject in it.
  • The Academy has a Broadcast Hub field in the inspector, which is list of brains used in the scene. To train or control your Brain from the mlagents-learn Python script, you need to drag the relevant LearningBrain ScriptableObjects used in your scene into entries into this list.

Migrating from ML-Agents toolkit v0.4 to v0.5

Important

• The Unity project unity-environment has been renamed UnitySDK.
• The python folder has been renamed to ml-agents. It now contains two packages, mlagents.env and mlagents.trainers. mlagents.env can be used to interact directly with a Unity environment, while mlagents.trainers contains the classes for training agents.
• The supported Unity version has changed from 2017.1 or later to 2017.4 or later. 2017.4 is an LTS (Long Term Support) version that helps us maintain good quality and support. Earlier versions of Unity might still work, but you may encounter an error listed here.

Unity API

• Discrete Actions now use branches. You can now specify concurrent discrete actions. You will need to update the Brain Parameters in the Brain Inspector in all your environments that use discrete actions. Refer to the discrete action documentation for more information.

Python API

• In order to run a training session, you can now use the command mlagents-learn instead of python3 learn.py after installing the mlagents packages. This change is documented here. For example, if we previously ran

  python3 learn.py 3DBall --train
  

  from the python subdirectory (which is changed to ml-agents subdirectory in v0.5), we now run

  mlagents-learn config/trainer_config.yaml --env=3DBall --train
  

  from the root directory where we installed the ML-Agents Toolkit.

• It is now required to specify the path to the yaml trainer configuration file when running mlagents-learn. For an example trainer configuration file, see trainer_config.yaml. An example of passing a trainer configuration to mlagents-learn is shown above.

• The environment name is now passed through the --env option.

• Curriculum learning has been changed. Refer to the curriculum learning documentation for detailed information. In summary:

  • Curriculum files for the same environment must now be placed into a folder. Each curriculum file should be named after the Brain whose curriculum it specifies.
  • min_lesson_length now specifies the minimum number of episodes in a lesson and affects reward thresholding.
  • It is no longer necessary to specify the Max Steps of the Academy to use curriculum learning.

Migrating from ML-Agents toolkit v0.3 to v0.4

Unity API

• using MLAgents; needs to be added in all of the C# scripts that use ML-Agents.

Python API

• We've changed some of the Python packages dependencies in requirement.txt file. Make sure to run pip3 install -e . within your ml-agents/python folder to update your Python packages.

Migrating from ML-Agents toolkit v0.2 to v0.3

There are a large number of new features and improvements in the ML-Agents toolkit v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.

Important

• The ML-Agents toolkit is no longer compatible with Python 2.

Python Training

• The training script ppo.py and PPO.ipynb Python notebook have been replaced with a single learn.py script as the launching point for training with ML-Agents. For more information on using learn.py, see here.
• Hyperparameters for training Brains are now stored in the trainer_config.yaml file. For more information on using this file, see here.

Unity API

• Modifications to an Agent's rewards must now be done using either AddReward() or SetReward().
• Setting an Agent to done now requires the use of the Done() method.
• CollectStates() has been replaced by CollectObservations(), which now no longer returns a list of floats.
• To collect observations, call AddVectorObs() within CollectObservations(). Note that you can call AddVectorObs() with floats, integers, lists and arrays of floats, Vector3 and Quaternions.
• AgentStep() has been replaced by AgentAction().
• WaitTime() has been removed.
• The Frame Skip field of the Academy is replaced by the Agent's Decision Frequency field, enabling the Agent to make decisions at different frequencies.
• The names of the inputs in the Internal Brain have been changed. You must replace state with vector_observation and observation with visual_observation. In addition, you must remove the epsilon placeholder.

Semantics

In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.

Old - v0.2 and earlier	New - v0.3 and later
State	Vector Observation
Observation	Visual Observation
Action	Vector Action
N/A	Text Observation
N/A	Text Action