The release contains three main components:
- A reward predictor that can be plugged into any agent and learns to predict the actions the agent could take that a human would approve of.
- An example agent that learns via a function specified by a reward predictor. RL-Teacher ships with three pre-integrated algorithms, including OpenAI Baselines PPO.
- A web-app that humans can use to give feedback, providing the data used to train the reward predictor.
The entire system consists of less than 1,000 lines of Python code (excluding the agents). After you’ve set up your web server you can launch an experiment by running:
$ python rl_teacher/teach.py -p human --pretrain_labels 175 -e Reacher-v1 -n human-175
Humans can give feedback via a simple web interface (shown above), which can be run locally (not recommended) or on a separate machine. Full documentation is available on the project’s GitHub repository. We’re excited to see what AI researchers and engineers do with this technology—please get in touch with any experimental results!