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Found 3 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 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
Adversarial attacks in consensus-based multi-agent reinforcement learning
Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
