Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks


  • Yijie Guo University of Michigan
  • Qiucheng Wu University of Michigan
  • Honglak Lee LG AI Research University of Michigan




Machine Learning (ML)


Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data drawn from training tasks to infer the common structure shared among tasks. Without heavy reward engineering, the sparse rewards in long-horizon tasks exacerbate the problem of sample efficiency in meta-RL. Another challenge in meta-RL is the discrepancy of difficulty level among tasks, which might cause one easy task dominating learning of the shared policy and thus preclude policy adaptation to new tasks. This work introduces a novel objective function to learn an action translator among training tasks. We theoretically verify that the value of the transferred policy with the action translator can be close to the value of the source policy and our objective function (approximately) upper bounds the value difference. We propose to combine the action translator with context-based meta-RL algorithms for better data collection and moreefficient exploration during meta-training. Our approach em-pirically improves the sample efficiency and performance ofmeta-RL algorithms on sparse-reward tasks.




How to Cite

Guo, Y., Wu, Q., & Lee, H. (2022). Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6792-6800. https://doi.org/10.1609/aaai.v36i6.20635



AAAI Technical Track on Machine Learning I