Yanchao Sun

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, …

Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning

2023 · arXiv (Cornell University)

Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL) by …

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 …

Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

2023 · arXiv (Cornell University)

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus …