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Mingkang Wu

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2 papers

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AAMAS Conference 2025 Conference Paper

On-Policy Reinforcement Learning From Failure via Sparse Reward Densification

Mingkang Wu
Yongcan Cao

This paper proposes a new reinforcement learning method that learns from failure under sparse reward environments. While traditional approaches rely on costly expert demonstrations to guide learning in sparse reward environments, this method uses readily available failures. The method trains a discriminator to measure the dissimilarity between the agent’s behaviors and failures, generating dense rewards. The method then uses this information to guide policy learning. Experimental results show this failure-based learning approach performs competitively with existing methods.

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AAAI Conference 2024 Conference Paper

Rating-Based Reinforcement Learning

Devin White
Mingkang Wu
Ellen Novoseller
Vernon J. Lawhern
Nicholas Waytowich
Yongcan Cao

This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach is based on human evaluation of individual trajectories without relative comparisons between sample pairs. The rating-based reinforcement learning approach builds on a new prediction model for human ratings and a novel multi-class loss function. We conduct several experimental studies based on synthetic ratings and real human ratings to evaluate the effectiveness and benefits of the new rating-based reinforcement learning approach.

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