Summary of Constrained Ensemble Exploration For Unsupervised Skill Discovery, by Chenjia Bai et al.
Constrained Ensemble Exploration for Unsupervised Skill Discovery
by Chenjia Bai, Rushuai Yang, Qiaosheng Zhang, Kang Xu, Yi Chen, Ting Xiao, Xuelong Li
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed unsupervised reinforcement learning (RL) framework combines empowerment-driven skill discovery with entropy-based exploration, enabling the learning of useful behaviors via reward-free pre-training. The novel approach uses an ensemble of skills that perform partition exploration based on state prototypes, allowing each skill to explore localized clusters and maximize overall state coverage. State-distribution constraints are used to regulate skill occupancy and desired cluster formation, leading to distinguishable skills. Theoretical analysis is provided for the impact of these constraints on state entropy and resulting skill distributions. Experimental results demonstrate that this approach learns well-explored ensemble skills and achieves superior performance in various downstream tasks compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn useful behaviors without being told what’s right or wrong. It combines two existing approaches, allowing each one to focus on specific areas of exploration. By doing so, it can learn many different skills that work together to cover the entire space. This approach is tested on several challenging tasks and shows better results than previous methods. |
Keywords
» Artificial intelligence » Reinforcement learning » Unsupervised
