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Poster
Policy Learning Using Weak Supervision
Jingkang Wang · Hongyi Guo · Zhaowei Zhu · Yang Liu

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ Virtual
in Poster Session 3 »

Most existing policy learning solutions require the learning agents to receive high-quality supervision signals, e.g., rewards in reinforcement learning (RL) or high-quality expert demonstrations in behavioral cloning (BC). These quality supervisions are either infeasible or prohibitively expensive to obtain in practice. We aim for a unified framework that leverages the available cheap weak supervisions to perform policy learning efficiently. To handle this problem, we treat the weak supervision'' as imperfect information coming from a peer agent, and evaluate the learning agent's policy based on a correlated agreement'' with the peer agent's policy (instead of simple agreements). Our approach explicitly punishes a policy for overfitting to the weak supervision. In addition to theoretical guarantees, extensive evaluations on tasks including RL with noisy reward, BC with weak demonstrations, and standard policy co-training (RL + BC) show that our method leads to substantial performance improvements, especially when the complexity or the noise of the learning environments is high.

Keywords: Reinforcement Learning and Planning

Author Information

Jingkang Wang (Uber ATG, University of Toronto)
Hongyi Guo (Shanghai Jiao Tong University)
Zhaowei Zhu (UC Santa Cruz)
Yang Liu (UC Santa Cruz)

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