50 citations · 78 across the 5 of their papers we have counts for
11 papers
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens +4
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our metho…
Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Liheng Chen +4
Centralized training with decentralized execution has become an important paradigm in multi-agent learning. Though practical, current methods rely on restrictive assumptions to dec…
Learning to Model Opponent Learning
Ian Davies, Zheng Tian, Jun Wang
Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. The adaptation and learning of oth…
Compositional ADAM: An Adaptive Compositional Solver
Rasul Tutunov, Minne Li, Alexander I. Cowen-Rivers +2
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values. We proof that C-ADAM converges…
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…