The Unity team has released the version 0.3 of their anticipated toolkit ML-Agents. The new release is jam-packed with features on the likes of Imitation Learning, Multi-Brain Training, On-Demand Decision-Making, and Memory-Enhanced Agents.
Here’s a quick look at what each of these features brings to the table:
ML-Agents v0.3 uses imitation learning for training agents. Imitation Learning uses demonstrations of the desired behavior in order to provide a learning signal to the agents. For v0.3, the team uses Behavioral Cloning as the choice of imitation learning algorithm. This works by collecting training data from a teacher agent, and then simply using it to directly learn a behavior.
Using Multi-Brain Training, one can train more than one brain at a time, with their separate observation and action space. At the end of training, there is only one binary (.bytes) file, which contains one neural network model per brain.
Agents ask for decisions in an on-demand fashion, rather than making decisions every step or every few steps of the engine. Users can enable and disable On-Demand Decision-Making for each agent independently with the click of a button!
The unity team has included two methods for dealing with partial observability within learning environments through Memory-Enhanced Agents.
Apart from these features, there is an addition of a Docker-Image, changes to API Semantics and a major revamp of the documentation. All this to make setup and usage simpler and more intuitive. Users can check the GitHub page to download the new version and learn all the details on the release page.
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