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 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.
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.