Summary of Constrained Intrinsic Motivation For Reinforcement Learning, by Xiang Zheng et al.
Constrained Intrinsic Motivation for Reinforcement Learning
by Xiang Zheng, Xingjun Ma, Chao Shen, Cong Wang
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Constrained Intrinsic Motivation (CIM) framework addresses two fundamental issues in reinforcement learning: designing an effective intrinsic objective for Reward-Free Pre-Training tasks and reducing bias introduced by the intrinsic objective in Exploration with Intrinsic Motivation tasks. Existing methods suffer from limitations such as static skills, limited state coverage, and sample inefficiency. CIM maximizes the conditional state entropy subject to an alignment constraint on the state encoder network for efficient skill discovery and state coverage. This approach outperforms fifteen IM methods in various MuJoCo robotics environments, demonstrating superior unsupervised skill discovery and fine-tuning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves two big problems with how we use motivation to learn new skills: designing a good way to measure progress without rewards and reducing mistakes when exploring new things. Right now, our approach has limitations like not discovering many new skills or not covering all the important states. To fix this, the authors created a new method called Constrained Intrinsic Motivation (CIM) that makes sure we’re making progress and not getting stuck in one way of doing things. They tested CIM on robots and it worked much better than other methods at finding new skills and adjusting to changing situations. |
Keywords
» Artificial intelligence » Alignment » Encoder » Fine tuning » Reinforcement learning » Unsupervised
