Summary of Towards Generalizable Reinforcement Learning Via Causality-guided Self-adaptive Representations, by Yupei Yang et al.
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
by Yupei Yang, Biwei Huang, Fan Feng, Xinyue Wang, Shikui Tu, Lei Xu
First submitted to arxiv on: 30 Jul 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 paper presents a novel reinforcement learning (RL) method that enables agents to generalize effectively across tasks with evolving dynamics. The proposed approach, called CSR, employs causality-guided self-adaptive representation-based learning to characterize the latent causal variables within the RL system. This allows the agent to determine whether changes in the environment stem from distribution shifts or variations in space, and to precisely locate these changes. The authors demonstrate the effectiveness of CSR by fine-tuning the causal model under different scenarios and show that it outperforms state-of-the-art baselines on a wide range of scenarios, including simulated environments, CartPole, CoinRun, and Atari games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for machines to learn from experience. It helps them adapt to changing situations by understanding the underlying causes of these changes. The approach is called CSR, and it’s better than existing methods at handling complex tasks that require quick learning. The authors tested CSR on various simulated environments, and it worked well even when the environment changed in unexpected ways. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
