ML-Agents Beta 0.7.0

New Features

• The Unity Inference Engine replaces TensorFlowSharp for Inference.
• There is a new icon for the neural network models.

Fixes & Performance Improvements

• Various improvements to documentation.
• New Baseline/Dopamine Gym documentation.
• Rename decision frequency to decision interval.
• Various bug fixes.
• Fixed Grpc imports on platforms that do not support training
• Minor Fix on Tennis CollectObservations

Known Issues

• IL2CPP backend for Windows Standalone does not allow training.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.7.0, as well as: @Du-z, @vxgu86, @Lucretiel

ML-Agents Beta 0.6.0a

Fixes and Improvements

• Fixes typo on documentation.
• Fixes Division by zero error when using recurrent and discrete control.
• Fixes UI bug on Learning Brain warnings with visual observations.
• Fixes Curriculum Learning Brain names.
• Fixes Ctrl-C bug on Windows in which the model would not be saved when training was interrupted.
• Fixes In Editor Training Bug with Docker.
• Fixes Docker Training Bug in which models would not be saved after training was interrupted.

ML-Agents Beta 0.6.0

Important

Brains have been changed to ScriptableObjects instead of MonoBehaviors. This will allow you to set Brains in prefabs and use the same Brains across multiple scenes. Please see Migrating from v0.5 to v0.6 documentation for more information.

• Internal and External Brain types have been replaced by a LearningBrain asset.
• Heuristic Brain type have been replaced by a HeuristicBrain asset.
• Player Brain type have been replaced by a PlayerBrain asset.
• Brains are now exposed to the Python training process through the "Broadcast Hub" within the Academy component.

New Features

• [Unity] Demonstration Recorder. It is now possible to record the actions and observations of an Agent from the Editor, and use them to train Agents at a later time. This allows you to reuse training data for multiple training sessions.
• [Communication] Added a make_for_win.bat file to generate the protobuf objects in protobuf-definitions on Windows machines.
• Added debug warnings to the LearningBrain when models are not compatibles with the Brain Parameters.

Changes

• Removed the graph scope from trained models. When training multiple Brains during the same session, one graph per Brain will be created instead of one single graph with multiple graph scopes.

Fixes & Performance Improvements

• Various improvements to documentation.

Known Issues

• Ending training early using CTL+C does not save the model on Windows.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.5.0, as well as: @eltronix, @bjmolitor, @luhairong, @YuMurata

ML-Agents Beta 0.5.0a

Fixes and Improvements

• Fixes typo on documentation.
• Removes unnecessary gitignore line.
• Fixes imitation learning scenes.
• Fixes BananaCollector environment.
• Enables gym_unity with multiple visual observations.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.5.0a, as well as: @Sohojoe, @fengredrum, and @xiaodi-faith.

ML-Agents Beta 0.5.0

Important

We have reorganized the project repository. Please see Migrating from v0.4 to v0.5 documentation for more information. Highlighted changes to repository structure include:

• The python folder has been renamed ml-agents. It now contains a python package called mlagents.
• The unity-environment folder, containing the Unity project, has been renamed UnitySDK.
• The protobuf definitions used for communication have been added to a new protobuf-definitions folder.
• Example curricula and the trainer configuration file have been moved to a new config sub-directory.

Environments

To learn more about new and improved environments, see our Example Environments page.

Improved

The following environments have been changes to use Multi Discrete Action:

• WallJump
• BananaCollector

The following environment has been modified to use Action Masking:

• GridWorld

New Features

• [Gym] New package gym-unity which provides gym interface to wrap UnityEnvironment. More information here.

• [Training] Can now run multiple concurent training sessions with the --num-runs=<n> command line option. (Training sessions are independent, and do not improve learning performance.)

• [Unity] Meta-Curriculum. Supports curriculum learning in multi-brain environments.

• [Unity] Action Masking for Discrete Control - It is now possible to mask invalid actions each step to limit the actions an agent can take.

• [Unity] Action Branches for Discrete Control - It is now possible to define discrete action spaces which contain multiple branches, each with its own space size.

Changes

• Can now visualize value estimates when using models trained with PPO from Unity with GetValueEstimate().
• It is now possible to specify which camera the Monitor displays to.
• Console summaries will now be displayed even when running inference mode from python.
• Minimum supported Unity version is now 2017.4.

Fixes & Performance Improvements

• Replaced some activation functions to swish.
• Visual Observations use PNG instead of JPEG to avoid compression losses.
• Improved python unit tests.
• Fix to enable multiple training sessions on single GPU.
• Curriculum lessons are now tracked correctly.

Known Issues

• Ending training early using CTL+C does not save the model on Windows.
• Sequentially opening and closing multiple instances of UnityEnvironment within a single process is not possible.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.5.0, as well as: @sterling000, @bartlomiejwolk, @Sohojoe, @Phantomb.

ML-Agents Toolkit Beta 0.4.0b

Fixes & Performance Improvements

• Corrects observation space description for PushBlock environment.
• Fixes bug preventing using environments with python multi-processing.
• Fixes bug preventing agents to be initialized without a brain.

ML-Agents Toolkit Beta 0.4.0a

Environments

• Changes to example environments for visual consistency.

Documentation

• Adjustments to Windows installation documentation.
• Updates documentation to refer to project as a toolkit.

Changes

• New Amazon Web Service AMI.
• Uses swish for continuous control activation function.
• Corrected version number in setup.py.

Fixes & Performance Improvements

• Fixes memory leak bug when using visual observations.
• Fixes use of behavioral cloning with visual observations.
• Fixes use of curiosity-driven exploration with on-demand decision making.
• Optimize visual observations when using internal brain.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.4.0a, as well as: @tcmxx

ML-Agents Beta 0.4.0

Environments

To learn more about new and improved environments, see our Example Environments page.

New

• Walker - Humanoid physics based agent. The agents must move its body toward the goal direction as quickly as possible without falling.

• Pyramids - Sparse reward environment. The agent must press a button, then topple a pyramid of blocks to get the golden brick at the top. Used to demonstrate Curiosity.

Improved

• Revamped the Crawler environment

• Added visual observation based scenes for :

  • BananaCollector
  • PushBlock
  • Hallway
  • Pyramids

• Added Imitation Learning based scenes for :

  • Tennis
  • Bouncer
  • PushBlock
  • Hallway
  • Pyramids

New Features

• [Unity] In Editor Training - It is now possible to train agents directly in the editor without building the scene. For more information, see here.

• [Training] Curiosity-Driven Exploration - Addition of curiosity-based intrinsic reward signal when using PPO. Enable by setting use_curiosity brain training hyperparameter to true.

• [Unity] Support for providing player input using axes within the Player Brain.

• [Unity] TensorFlowSharp Plugin has been upgraded to version 1.7.1.

Changes

• Main ML-Agents code now within MLAgents namespace. Ensure that the MLAgents namespace is added to necessary project scripts such as Agent classes.
• ASCII art added to learn.py script.
• Communication now uses gRPC and Protobuf. JSON libraries removed.
• TensorBoard now reports mean absolute loss as opposed to total loss update loop.
• PPO algorithm now uses wider gaussian output for Continuous Control models (increasing performance).

Documentation

• Added Quick Start and & FAQ sections to the documentation.
• Added documentation explaining how to use ML-Agents on Microsoft Azure.
• Added benchmark reward thresholds for example environments.

Fixes & Performance Improvements

• Episode length is now properly reported in TensorBoard in the first episode.
• Behavioral Cloning now works with LSTM models.

Known Issues

• Curiosity-driven exploration does not function with On-Demand Decision Making. Expect a fix in v0.4.0a.

Acknowledgements

Thanks to everyone at Unity who contributed to v0.4, as well as: @sterlingcrispin, @ChrisRisner, @akmadian, @animaleja32, @LeighS, and @5665tm.

ML-Agents Beta 0.3.1b

Fixes

• Behavioral cloning fix (use stored info rather than previous info)
• Value Bootstrap fixed for ppo

ML-Agents Beta 0.3.1a

Fixes

• Remove links to out of date Unity Packages
• Fix to the CoreInternalBrain for discrete vector observations
• Retraining of the Basic Environment
• Fixed the normalization of images in the internal brain