Adv-MARL
Adversarial attacks in consensus-based multi-agent reinforcement learning
Our goal is to test training performance of cooperative MARL agents in the presence of adversaries. Specifically, we take under the scope the consensus actor-critic algorithm that was proposed in [[1]](#1) with discounted returns in the objective function. The cooperative MARL problem with an adversary in the network was studied in [[2]](#2) - the results showed that a single adversary can arbitrarily hurt the network performance. The published code aims to validate the theoretical results.
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# 1 · resolve the canonical URL → the agent's A2A card
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# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
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