Resolved Q-Learning implementation

We don't currently have a DQN implementation in ML-Agents, though we do support Soft Actor-Critic, which is similarly an off-policy algorithm that learns a Q-function.

You can plug in your own algorithms using the Python API (https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md) or the Gym wrapper (https://github.com/Unity-Technologies/ml-agents/blob/master/gym-unity/README.md).

 

We don't currently have an interface for writing a custom trainer using mlagents-learn.

If you want to use the low-level python API to interact with the environment, that's possible. There is an example of this in a Google colab here: https://colab.research.google.com/d...GnynYdL_LRsHG#forceEdit=true&sandboxMode=true

 

@lgendrot Custom trainers are something that we'd like to do in the future, but still no definite timelines. We took the first steps towards a plugin system here - we would take a similar approach for registration of custom trainers, but I think we'd need to do more working making our training code more modular and reusable.