103 citations · 273 across the 23 of their papers we have counts for
8 papers · 1 filter
Multi-View Reinforcement Learning
Minne Li, Lisheng Wu, Haitham Bou Ammar +1
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models…
Bi-level Actor-Critic for Multi-agent Coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang +4
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the…
Wasserstein Robust Reinforcement Learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar +4
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}…
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Rui Luo, Qiang Zhang, Yaodong Yang +1
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated mode…
A Regularized Opponent Model with Maximum Entropy Objective
Zheng Tian, Ying Wen, Zhichen Gong +3
In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In…
Joint Perception and Control as Inference with an Object-based Implementation
Minne Li, Zheng Tian, Pranav Nashikkar +3
Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a…