Ruijie Zheng

Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

2022 · arXiv (Cornell University)

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, …

COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

2023 · arXiv (Cornell University)

Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to the complex real-world environment, it is …