Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents* behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.
 The paper proposes a new environment - 2vs2 soccer - to study emergence of multi-agent coordinated team behaviors. Learning relies on population-based training of agent*s shaped reward mixtures and approach of nash averaging is used for evaluation. Clarity: the paper is well-written and clear. The ablations provided are helpful in understanding how much different introduced components matter, and quantitative and qualitative analysis of resulting behavior is quite nice Originality: the individual pieces of this work (PBT, SVG, nash averaging) have been introduced previously, but this paper puts them together in a well-chosen manner. Significance: I believe this paper proposes a number of interesting observations (effects of PBT, evaluation, effects of recurrent policies to overcome non-stationarity issues) that I believe would be of value to the part of ICLR community doing research in multi-agent systems.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents* behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.
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 proposes a new environment - 2vs2 soccer - to study emergence of multi-agent coordinated team behaviors. Learning relies on population-based training of agent*s shaped reward mixtures and approach of nash averaging is used for evaluation. Clarity: the paper is well-written and clear. The ablations provided are helpful in understanding how much different introduced components matter, and quantitative and qualitative analysis of resulting behavior is quite nice Originality: the individual pieces of this work (PBT, SVG, nash averaging) have been introduced previously, but this paper puts them together in a well-chosen manner. Significance: I believe this paper proposes a number of interesting observations (effects of PBT, evaluation, effects of recurrent policies to overcome non-stationarity issues) that I believe would be of value to the part of ICLR community doing research in multi-agent systems.
assistant
 The paper proposes a new environment - 2vs2 soccer - to study emergence of multi-agent coordinated team behaviors. Learning relies on population-based training of agent*s shaped reward mixtures and approach of nash averaging is used for evaluation. Clarity: the paper is well-written and clear. The ablations provided are helpful in understanding how much different introduced components matter, and quantitative and qualitative analysis of resulting behavior is quite nice Originality: the individual pieces of this work (PBT, SVG, nash averaging) have been introduced previously, but this paper puts them together in a well-chosen manner. Significance: I believe this paper proposes a number of interesting observations (effects of PBT, evaluation, effects of recurrent policies to overcome non-stationarity issues) that I believe would be of value to the part of ICLR community doing research in multi-agent systems.