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
 This paper introduces a new method for multi-agent reinforcement learning. The proposed algorithm -- which uses shared critics at training time but individual policies at test time -- makes use of a specialised attention mechanism. The benefits include better scalability (as the dependency of the inputs is linear in the number of agents, rather than quadratic), and also being more amenable to diverse reward and action structures than the previous work. ---------Quality and clarity--------- The paper is nicely written, and the ideas are developed in a clear fashion, if slightly verbose (the first 3 pages, though informative, might have been condensed a bit to make more room for the new algorithm). The problem is well-motivated and the benefits of the new algorithm are well showcased. One negative point that does stick out is the bibliography, where papers that have been published for years (e.g. the Adam paper) are still referenced as arXiv preprints. ---------Originality and significance---------- Although attentive mechanisms have been around for a while, their use in this specific setting (learning shared critics for multi-agent RL) is, and yields desirable properties. The new algorithm opens the door for training in more complex environments, with a larger number of agents (although the number is still limited in the presented experiments). The main issue I do see with the paper is its experimental section. The two tasks are picked to showcase the benefits of the new approach. This does mean that the competing algorithms have to undergo significant changes (at least in the case of the DDPG-based methods), which takes away from the validity of the comparison. Ideally, there would be at least one other task on which the other algorithms have been trained on by their respective authors. As mentioned right before Section 4, MAAC can be used on continuous action spaces at the price of increased computational cost, so this should be doable. Overall, this is a nicely written paper which introduces an interesting new method for multi-agent RL, with promising initial results. A more thorough experimental section with slightly fairer comparisons would increase its quality significantly. Pros - clear paper, easy to read - interesting application of attention mechanism to multi-agent RL - promising initial results Cons - no comparison to related algorithms on tasks where they have already been evaluated externally - the amount of workers is still quite limited in the experiments
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>



 This paper introduces a new method for multi-agent reinforcement learning. The proposed algorithm -- which uses shared critics at training time but individual policies at test time -- makes use of a specialised attention mechanism. The benefits include better scalability (as the dependency of the inputs is linear in the number of agents, rather than quadratic), and also being more amenable to diverse reward and action structures than the previous work. ---------Quality and clarity--------- The paper is nicely written, and the ideas are developed in a clear fashion, if slightly verbose (the first 3 pages, though informative, might have been condensed a bit to make more room for the new algorithm). The problem is well-motivated and the benefits of the new algorithm are well showcased. One negative point that does stick out is the bibliography, where papers that have been published for years (e.g. the Adam paper) are still referenced as arXiv preprints. ---------Originality and significance---------- Although attentive mechanisms have been around for a while, their use in this specific setting (learning shared critics for multi-agent RL) is, and yields desirable properties. The new algorithm opens the door for training in more complex environments, with a larger number of agents (although the number is still limited in the presented experiments). The main issue I do see with the paper is its experimental section. The two tasks are picked to showcase the benefits of the new approach. This does mean that the competing algorithms have to undergo significant changes (at least in the case of the DDPG-based methods), which takes away from the validity of the comparison. Ideally, there would be at least one other task on which the other algorithms have been trained on by their respective authors. As mentioned right before Section 4, MAAC can be used on continuous action spaces at the price of increased computational cost, so this should be doable. Overall, this is a nicely written paper which introduces an interesting new method for multi-agent RL, with promising initial results. A more thorough experimental section with slightly fairer comparisons would increase its quality significantly. Pros - clear paper, easy to read - interesting application of attention mechanism to multi-agent RL - promising initial results Cons - no comparison to related algorithms on tasks where they have already been evaluated externally - the amount of workers is still quite limited in the experiments
assistant
 This paper introduces a new method for multi-agent reinforcement learning. The proposed algorithm -- which uses shared critics at training time but individual policies at test time -- makes use of a specialised attention mechanism. The benefits include better scalability (as the dependency of the inputs is linear in the number of agents, rather than quadratic), and also being more amenable to diverse reward and action structures than the previous work. ---------Quality and clarity--------- The paper is nicely written, and the ideas are developed in a clear fashion, if slightly verbose (the first 3 pages, though informative, might have been condensed a bit to make more room for the new algorithm). The problem is well-motivated and the benefits of the new algorithm are well showcased. One negative point that does stick out is the bibliography, where papers that have been published for years (e.g. the Adam paper) are still referenced as arXiv preprints. ---------Originality and significance---------- Although attentive mechanisms have been around for a while, their use in this specific setting (learning shared critics for multi-agent RL) is, and yields desirable properties. The new algorithm opens the door for training in more complex environments, with a larger number of agents (although the number is still limited in the presented experiments). The main issue I do see with the paper is its experimental section. The two tasks are picked to showcase the benefits of the new approach. This does mean that the competing algorithms have to undergo significant changes (at least in the case of the DDPG-based methods), which takes away from the validity of the comparison. Ideally, there would be at least one other task on which the other algorithms have been trained on by their respective authors. As mentioned right before Section 4, MAAC can be used on continuous action spaces at the price of increased computational cost, so this should be doable. Overall, this is a nicely written paper which introduces an interesting new method for multi-agent RL, with promising initial results. A more thorough experimental section with slightly fairer comparisons would increase its quality significantly. Pros - clear paper, easy to read - interesting application of attention mechanism to multi-agent RL - promising initial results Cons - no comparison to related algorithms on tasks where they have already been evaluated externally - the amount of workers is still quite limited in the experiments