CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others* success, and credit-assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.
This paper presents a method for adding credit assignment to multi-agent RL and proposes a way of adding a curriculum to the training process. The best heuristics and structures to incorporate in the modeling, learning, and exploration parts of multi-agent RL are still largely unknown and this paper explores some reasonable new ones. In most of the tasks this is evaluated on in Figure 5 this approach adds a slight improvement to the SOTA and I think an exciting direction of future work is to continue pushing on multi-agent RL in even more complex envirnoments where the SOTA break down in even worse ways.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others* success, and credit-assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.
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 presents a method for adding credit assignment to multi-agent RL and proposes a way of adding a curriculum to the training process. The best heuristics and structures to incorporate in the modeling, learning, and exploration parts of multi-agent RL are still largely unknown and this paper explores some reasonable new ones. In most of the tasks this is evaluated on in Figure 5 this approach adds a slight improvement to the SOTA and I think an exciting direction of future work is to continue pushing on multi-agent RL in even more complex envirnoments where the SOTA break down in even worse ways.
assistant
This paper presents a method for adding credit assignment to multi-agent RL and proposes a way of adding a curriculum to the training process. The best heuristics and structures to incorporate in the modeling, learning, and exploration parts of multi-agent RL are still largely unknown and this paper explores some reasonable new ones. In most of the tasks this is evaluated on in Figure 5 this approach adds a slight improvement to the SOTA and I think an exciting direction of future work is to continue pushing on multi-agent RL in even more complex envirnoments where the SOTA break down in even worse ways.