ml-agents/docs/Migrating.md

Migrating

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 BananaCollector). In our examples, groups of Agents can be reset through an "Area" that can reset groups of Agents.

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 packges into two seperate 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