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Found 5 papers, 1 papers with code
Cooperative Actor-Critic via TD Error Aggregation
In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Resilient Constrained Consensus over Complete Graphs via Feasibility Redundancy
This paper considers a resilient high-dimensional constrained consensus problem and studies a resilient distributed algorithm for complete graphs.
Finite-Time Error Bounds for Distributed Linear Stochastic Approximation
While the convergence of consensus-based stochastic approximation algorithms when the interconnection among the agents is described by doubly stochastic matrices (at least in expectation) has been studied, less is known about the case when the interconnection matrix is simply stochastic.
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation
We show that in the presence of Byzantine agents, whose estimation and communication strategies are completely arbitrary, the estimates of the cooperative agents converge to a bounded consensus value with probability one, provided that there are at most $H$ Byzantine agents in the neighborhood of each cooperative agent and the network is $(2H+1)$-robust.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors.
