Enhancing Multi-Agent Collaboration with Attention-Based Actor-Critic Policies

• URL: http://arxiv.org/abs/2507.22782v2
• Date: Thu, 31 Jul 2025 16:47:21 GMT
• Title: Enhancing Multi-Agent Collaboration with Attention-Based Actor-Critic Policies
• Authors: Hugo Garrido-Lestache, Jeremy Kedziora,
• Abstract summary: Team-Attention-Actor-Critic (TAAC) is a learning algorithm designed to enhance multi-agent collaboration in cooperative environments.<n>We evaluate TAAC in a simulated soccer environment against benchmark algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Team-Attention-Actor-Critic (TAAC), a reinforcement learning algorithm designed to enhance multi-agent collaboration in cooperative environments. TAAC employs a Centralized Training/Centralized Execution scheme incorporating multi-headed attention mechanisms in both the actor and critic. This design facilitates dynamic, inter-agent communication, allowing agents to explicitly query teammates, thereby efficiently managing the exponential growth of joint-action spaces while ensuring a high degree of collaboration. We further introduce a penalized loss function which promotes diverse yet complementary roles among agents. We evaluate TAAC in a simulated soccer environment against benchmark algorithms representing other multi-agent paradigms, including Proximal Policy Optimization and Multi-Agent Actor-Attention-Critic. We find that TAAC exhibits superior performance and enhanced collaborative behaviors across a variety of metrics (win rates, goal differentials, Elo ratings, inter-agent connectivity, balanced spatial distributions, and frequent tactical interactions such as ball possession swaps).

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