github Unity-Technologies/ml-agents 0.10.0
ML-Agents Beta 0.10.0

latest releases: release_21_docs, release_21, python-packages_1.0.0...
pre-release4 years ago

New Features

  • Soft Actor-Critic (SAC) is added as a new trainer option, complementing Proximal Policy Optimization (PPO). SAC, an off-policy algorithm, is more sample-efficient (i.e., requires fewer environment steps). For environments that take a long time to execute a step (about >0.1 second or greater) this can lead to dramatic training speedups of around 3-5 times versus PPO. In addition to sample-efficiency, SAC has been shown to be robust to small variations in the environment and effective at exploring the environment to find optimal behaviors. See the SAC documentation for more details.
  • Example environments have been updated to a new dark-theme visual style and colors have been standardized across all environments.
  • Unity environment command line arguments can be passed through mlagents-learn. See the documentation on how to use this feature.

Fixes and Improvements

  • ML-Agents is now compatible with Python v3.7 and newer versions of Tensorflow up to 1.14.
  • Fixed an issue when using recurrent networks and agents are destroyed. (#2549)
  • Fixed a memory leak during inference. (#2541)
  • The UnitySDK.log is no longer logged out, which fixes an issue with 2019 versions of the Unity Editor (#2580).
  • The Academy class no longer has a Done() method. All Done calls should be handled in the Agent (#2519). See Migrating for more information.
  • C# code was updated to follow Unity coding conventions.
  • Fixed a crash that happens when enabling VAIL with a GAIL reward signal (#2598)
  • Other minor documentation enhancements and bug fixes.


  • Thanks to @tomatenbrei and everyone at Unity who contributed to v0.10.0.

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