ML-Agents Release 8

Package Versions

NOTE: It is strongly recommended that you use packages from the same release together for the best experience.

Major Features and Improvements

com.unity.ml-agents (C#)

• Stacking for compressed observations is now supported. An additional setting option Observation Stacks is added to the sensor components that support compressed observations. A new class ISparseChannelSensor with an additional method GetCompressedChannelMapping()is added to generate a mapping of the channels in compressed data to the actual channel after decompression for the python side to decompress correctly. (#4476)

ml-agents (Python)

• Added the Random Network Distillation (RND) intrinsic reward signal to the Pytorch trainers. To use RND, add a rnd section to the reward_signals section of your yaml configuration file. More information here. (#4473)
• The Communication API was changed to 1.2.0 to indicate support for stacked compressed observation. A new entry compressed_channel_mapping is added to the proto to handle decompression correctly. Newer versions of the package that wish to make use of this will also need a compatible version of the Python trainers. (#4476)

Bug Fixes and Minor Changes

com.unity.ml-agents (C#)

• Fixed a bug where accessing the Academy outside of play mode would cause the Academy to get stepped multiple times when in play mode. (#4532)

ml-agents (Python)

• In the VisualFoodCollector scene, a vector flag representing the frozen state of the agent is added to the input observations in addition to the original first-person camera frame. The scene is able to train with the provided default config file. (#4511)
• Added a new visual scene Visual3DBall in the 3DBall example. (#4513)
• Added conversion to string for sampler classes to increase the verbosity of the curriculum lesson changes. The lesson updates would now output the sampler stats in addition to the lesson and parameter name to the console. (#4484)
• Localized documentation in Russian is added. (#4529)

Acknowledgements

Thank you @SergeyMatrosov for your contributions to this release.