2 citations · 2 across the 4 of their papers we have counts for
4 papers
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
Andrea Baisero, Rupali Bhati, Shuo Liu +2
Value function decomposition methods for cooperative multi-agent reinforcement learning compose joint values from individual per-agent utilities, and train them using a joint objec…
Safe Multiagent Coordination via Entropic Exploration
Ayhan Alp Aydeniz, Enrico Marchesini, Robert Loftin +2
Many real-world multiagent learning problems involve safety concerns. In these setups, typical safe reinforcement learning algorithms constrain agents' behavior, limiting explorati…
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Enrico Marchesini, Andrea Baisero, Rupali Bhati +1
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full jus…
Equivariant Reinforcement Learning under Partial Observability
Hai Nguyen, Andrea Baisero, David Klee +3
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable d…