Unity releases ML-Agents toolkit v0.5 with Gym interface, a new suite of learning environments

In their commitment to become the go-to platform for Artificial Intelligence, Unity has released a new version of their ML-Agents Toolkit.  ML-Agents toolkit v0.5 comes with more flexible action specification, a Gym interface for researchers to more easily integrate ML-Agents environments into their training workflows, and a new suite of learning environments replicating some of the Continuous Control benchmarks used in Deep Reinforcement Learning.

They have also released a research paper on ML-Agents which the Unity platform has titled “Unity: A General Platform for Intelligent Agent.”

Changes to the ML-Agents toolkit v0.5

A lot of changes have been made pertaining to ML-Agents toolkit v0.5.

Highlighted changes to repository structure

• 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.

New features

• New package gym-unity which provides gym interface to wrap UnityEnvironment.
• The ML-Agents toolkit v0.5 can now run multiple concurrent training sessions with the --num-runs=<n> command line option.
• Added Meta-Curriculum which supports curriculum learning in multi-brain environments.
• Action Masking for Discrete Control which makes it possible to mask invalid actions each step to limit the actions an agent can take.
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Fixes & Performance Improvements

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

You can read all about the new version of ML-Agents Toolkit on the Unity Blog.

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