1 citations · 2 across the 9 of their papers we have counts for
9 papers
Learning Partial Action Replacement in Offline MARL
Yue Jin, Giovanni Montana
Offline multi-agent reinforcement learning (MARL) faces a critical challenge: the joint action space grows exponentially with the number of agents, making dataset coverage exponent…
Action-Free Offline-to-Online RL via Discretised State Policies
Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana
Most existing offline RL methods presume the availability of action labels within the dataset, but in many practical scenarios, actions may be missing due to privacy, storage, or s…
Partial Action Replacement: Tackling Distribution Shift in Offline MARL
Yue Jin, Giovanni Montana
Offline multi-agent reinforcement learning (MARL) is severely hampered by the challenge of evaluating out-of-distribution (OOD) joint actions. Our core finding is that when the beh…
Evaluation-Time Policy Switching for Offline Reinforcement Learning
Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana
Offline reinforcement learning (RL) looks at learning how to optimally solve tasks using a fixed dataset of interactions from the environment. Many off-policy algorithms developed…
Investigating Relational State Abstraction in Collaborative MARL
Sharlin Utke, Jeremie Houssineau, Giovanni Montana
This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is…
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing
Yue Jin, Shuangqing Wei, Giovanni Montana
In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons a…
Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning
Mianchu Wang, Yue Jin, Giovanni Montana
Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without interacting with the environment. A common challenge is handling multi-modal action…
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
Ting Zhu, Yue Jin, Jeremie Houssineau +1
In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favo…
State-Constrained Offline Reinforcement Learning
Charles A. Hepburn, Yue Jin, Giovanni Montana
Traditional offline reinforcement learning (RL) methods predominantly operate in a batch-constrained setting. This confines the algorithms to a specific state-action distribution p…