Summary of Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning, by Mianchu Wang et al.
Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning
by Mianchu Wang, Yue Jin, Giovanni Montana
First submitted to arxiv on: 4 Dec 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 In this paper, the authors propose a new approach to offline reinforcement learning (RL) that handles multi-modal action distributions. Offline RL seeks to learn optimal policies from static datasets without interacting with the environment, which is an important problem as it enables learning from logged data or simulations. The common challenge in offline RL is handling multi-modal action distributions, where multiple behaviors are represented in the data. Existing methods often assume unimodal behavior policies, leading to suboptimal performance when this assumption is violated. To address this issue, the authors propose a novel approach called weighted imitation Learning on One Mode (LOM). LOM focuses on learning from a single, promising mode of the behavior policy by using a Gaussian mixture model to identify modes and selecting the best mode based on expected returns. Theoretically, the authors show that LOM improves performance while maintaining simplicity in policy learning. Empirically, LOM outperforms existing methods on standard D4RL benchmarks and demonstrates its effectiveness in complex, multi-modal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning can learn optimal policies from static datasets without interacting with the environment. This is important because it enables learning from logged data or simulations. The challenge is handling multiple behaviors represented in the data. A new approach called LOM focuses on learning from a single behavior policy. This approach is better than existing methods. |
Keywords
» Artificial intelligence » Mixture model » Multi modal » Reinforcement learning
