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Found 4 papers, 2 papers with code
${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Identification and analysis of symmetrical patterns in the natural world have led to significant discoveries across various scientific fields, such as the formulation of gravitational laws in physics and advancements in the study of chemical structures.
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Executing actions in a correlated manner is a common strategy for human coordination that often leads to better cooperation, which is also potentially beneficial for cooperative multi-agent reinforcement learning (MARL).
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games
Our focus in this paper is to study the convergence of the policy gradient method for solving MPGs under softmax policy parameterization, both tabular and parameterized with general function approximators such as neural networks.
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing.
