Summary of Residual Learning and Context Encoding For Adaptive Offline-to-online Reinforcement Learning, by Mohammadreza Nakhaei et al.
Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
by Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Offline reinforcement learning (RL) allows for sequential behavior learning from fixed datasets. However, existing methods assume constant transition dynamics during offline and online phases. This assumption often breaks down in real-world applications, such as outdoor construction or navigation over rough terrain, where dynamics change between phases. To address this challenge, we propose a residual learning approach that infers dynamic changes to correct offline solution outputs. At the online fine-tuning phase, we train a context encoder to learn a representation consistent within the current environment while predicting dynamic transitions. Our experiments in modified D4RL MuJoCo environments demonstrate our approach’s ability to adapt to changing dynamics and generalize to unseen perturbations in a sample-efficient manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about learning how to make good decisions when faced with new situations that are different from what was previously learned. When we try to teach machines to learn from past experiences, we assume that the rules of the game stay the same. But in real life, things can change suddenly, like moving from a smooth road to rough terrain. The paper proposes a way to deal with these changes by learning how to adapt to new situations. It tests this idea in simulated environments and shows that it works well and is efficient. |
Keywords
* Artificial intelligence * Encoder * Fine tuning * Reinforcement learning
