Zongqing Lu

Informative Policy Representations in Multi-Agent Reinforcement Learning via Joint-Action Distributions.

2021 · arXiv (Cornell University)

In multi-agent reinforcement learning, the inherent non-stationarity of the environment caused by other agents' actions posed significant difficulties for an agent to learn a good policy independently. One way to deal with non-stationarity is agent …

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

2022 · arXiv (Cornell University)

The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. …