Summary of Towards An Information Theoretic Framework Of Context-based Offline Meta-reinforcement Learning, by Lanqing Li et al.
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
by Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Yang Yu, Junqiao Zhao, Pheng-Ann Heng
First submitted to arxiv on: 4 Feb 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 advent of offline meta-reinforcement learning (OMRL) has enabled RL agents to multi-task and quickly adapt while acquiring knowledge safely. Context-based OMRL (COMRL), in particular, aims to learn a universal policy conditioned on effective task representations. This paper proposes a unified framework by integrating key milestones in the field of COMRL. The authors show that pre-existing COMRL algorithms optimize the same mutual information objective between the task variable and its latent representation by implementing various approximate bounds. This theoretical insight offers design freedom for novel algorithms. The authors propose supervised and self-supervised implementations of this objective, demonstrating generalization across RL benchmarks, context shift scenarios, data qualities, and deep learning architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach machines how to make decisions quickly and safely. It combines two ideas: offline reinforcement learning (RL) and meta-RL. The goal is to help machines learn multiple tasks at once and adapt to new situations easily. The authors look at previous work in this area and show that it’s all connected by a common idea called mutual information. They then propose some new ways to use this idea to make machines even better at making decisions. This could lead to big advances in areas like artificial intelligence, decision-making, and problem-solving. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Multi task * Reinforcement learning * Self supervised * Supervised
