Summary of Individual Contributions As Intrinsic Exploration Scaffolds For Multi-agent Reinforcement Learning, by Xinran Li et al.
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
by Xinran Li, Zifan Liu, Shibo Chen, Jun Zhang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to exploration in multi-agent reinforcement learning (MARL) called Individual Contributions as intrinsic Exploration Scaffolds (ICES). ICES assesses each agent’s contribution from a global view, constructing exploration scaffolds with Bayesian surprise that guide individual agents towards actions impacting global latent state transitions. The approach separates exploration policies from exploitation policies, allowing the former to utilize privileged global information during training. Experimental results on cooperative benchmark tasks with sparse rewards, including Google Research Football (GRF) and StarCraft Multi-agent Challenge (SMAC), show ICES outperforms baselines in terms of exploration capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists came up with a new way to help robots learn by themselves in complex situations. They want to make sure these robots explore and find good ways to do things without getting stuck or confused. To do this, they created a system that looks at what each robot is doing and tries to encourage them to try new things by giving them rewards for actions that are important for the whole group. This helps the robots learn faster and better than before. |
Keywords
* Artificial intelligence * Reinforcement learning
