RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
This simulated robot is being trained to do ballet via a human giving feedback. It’s not obvious how to specify a reward function to achieve the same behavior.
The release contains three main components:
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:
`1$ 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’sGitHub repository(opens in a new window). We’re excited to see what AI researchers and engineers do with this technology—pleaseget in touchwith any experimental results!
Tom Brown, Dario Amodei, Paul Christiano
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