Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models

no code implementations • 15 Jan 2024 • Xingzhou Lou, Junge Zhang, Ziyan Wang, Kaiqi Huang, Yali Du

Through the use of pre-trained LMs and the elimination of the need for a ground-truth cost, our method enhances safe policy learning under a diverse set of human-derived free-form natural language constraints.

Reinforcement Learning (RL) Safe Reinforcement Learning

TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient

1 code implementation • 25 Dec 2023 • Xingzhou Lou, Junge Zhang, Timothy J. Norman, Kaiqi Huang, Yali Du

We propose Topology-based multi-Agent Policy gradiEnt (TAPE) for both stochastic and deterministic MAPG methods.

PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination

1 code implementation • 16 Jan 2023 • Xingzhou Lou, Jiaxian Guo, Junge Zhang, Jun Wang, Kaiqi Huang, Yali Du

We conduct experiments on the Overcooked environment, and evaluate the zero-shot human-AI coordination performance of our method with both behavior-cloned human proxies and real humans.