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Playing Non-Embedded Card-Based Games with Reinforcement Learning
Tianyang Wu, Lipeng Wan, Yuhang Wang +2
Significant progress has been made in AI for games, including board games, MOBA, and RTS games. However, complex agents are typically developed in an embedded manner, directly acce…
Bootstrapped Model Predictive Control
Yuhang Wang, Hanwei Guo, Sizhe Wang +2
Model Predictive Control (MPC) has been demonstrated to be effective in continuous control tasks. When a world model and a value function are available, planning a sequence of acti…
MInCo: Mitigating Information Conflicts in Distracted Visual Model-based Reinforcement Learning
Shiguang Sun, Hanbo Zhang, Zeyang Liu +4
Existing visual model-based reinforcement learning (MBRL) algorithms with observation reconstruction often suffer from information conflicts, making it difficult to learn compact r…
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation
Zhihong Liu, Long Qian, Zeyang Liu +3
Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the…
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
Zeyang Liu, Lipeng Wan, Xinrui Yang +3
Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intr…