Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems
 The paper considers an actor-critic scheme for multiagent RL, where the critic is specific to each agent and has access to all other agents* embedded observations. The main idea is to use an attention mechanism in the critic that learns to selectively scale the contributions of the other agents. The paper presents sufficient motivation and background, and the proposed algorithmic implementation seems reasonable. The proposed scheme is compared to two recent algorithms for centralized training of decentralized policies, and shows comparable or better results on two synthetic multiagent problems. I believe that the idea and approach of the paper are interesting and contribute to the multiagent learning literature. Regarding cons: - The critical structural choices (such as the attention model in section 3.2) are presented without too much justification, discussion of alternatives, etc. - The experiments show the learning results, but do not provide a peak "under the hood" to understand the way attention evolved and contributed to the results. - The experiments show good results compared to existing algorithms, but not impressively so.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper considers an actor-critic scheme for multiagent RL, where the critic is specific to each agent and has access to all other agents* embedded observations. The main idea is to use an attention mechanism in the critic that learns to selectively scale the contributions of the other agents. The paper presents sufficient motivation and background, and the proposed algorithmic implementation seems reasonable. The proposed scheme is compared to two recent algorithms for centralized training of decentralized policies, and shows comparable or better results on two synthetic multiagent problems. I believe that the idea and approach of the paper are interesting and contribute to the multiagent learning literature. Regarding cons: - The critical structural choices (such as the attention model in section 3.2) are presented without too much justification, discussion of alternatives, etc. - The experiments show the learning results, but do not provide a peak "under the hood" to understand the way attention evolved and contributed to the results. - The experiments show good results compared to existing algorithms, but not impressively so.
assistant
 The paper considers an actor-critic scheme for multiagent RL, where the critic is specific to each agent and has access to all other agents* embedded observations. The main idea is to use an attention mechanism in the critic that learns to selectively scale the contributions of the other agents. The paper presents sufficient motivation and background, and the proposed algorithmic implementation seems reasonable. The proposed scheme is compared to two recent algorithms for centralized training of decentralized policies, and shows comparable or better results on two synthetic multiagent problems. I believe that the idea and approach of the paper are interesting and contribute to the multiagent learning literature. Regarding cons: - The critical structural choices (such as the attention model in section 3.2) are presented without too much justification, discussion of alternatives, etc. - The experiments show the learning results, but do not provide a peak "under the hood" to understand the way attention evolved and contributed to the results. - The experiments show good results compared to existing algorithms, but not impressively so.