@inproceedings{
teodorescu2023codeplay,
title={Codeplay: Autotelic Learning through Collaborative Self-Play in Programming Environments},
author={Laetitia Teodorescu and C{\'e}dric Colas and Matthew Bowers and Thomas Carta and Pierre-Yves Oudeyer},
booktitle={Intrinsically-Motivated and Open-Ended Learning Workshop @NeurIPS2023},
year={2023},
url={https://openreview.net/forum?id=GgzfIBxa18}
}
Autotelic learning is the training setup where agents learn by setting their own goals and trying to achieve them. However, creatively generating freeform goals is challenging for autotelic agents. We present Codeplay, an algorithm casting autotelic learning as a game between a Setter agent and a Solver agent, where the Setter generates programming puzzles of appropriate difficulty and novelty for the solver and the Solver learns to achieve them. Early experiments with the Setter demonstrates one can effectively control the tradeoff between difficulty of a puzzle and its novelty by tuning the reward of the Setter, a code language model finetuned with deep reinforcement learning.
Codeplay: Autotelic Learning through Collaborative Self-Play in Programming Environments
Laetitia Teodorescu Cédric Colas Matthew Bowers Thomas Carta Pierre-Yves Oudeyer