Summary of Bayes Adaptive Monte Carlo Tree Search For Offline Model-based Reinforcement Learning, by Jiayu Chen et al.
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
by Jiayu Chen, Wentse Chen, Jeff Schneider
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel framework for offline reinforcement learning (RL) that addresses the uncertainty of the true Markov Decision Process (MDP). The approach is based on Bayes Adaptive Markov Decision Process (BAMDP), which models offline model-based RL as a principled framework for dealing with model uncertainty. A novel algorithm, Bayes Adaptive Monte-Carlo planning, is introduced to solve BAMDPs in continuous state and action spaces with stochastic transitions. This algorithm can be integrated into offline MBRL as a policy improvement operator in policy iteration. The proposed “RL + Search” framework improves on current offline RL methods by incorporating more computation input, similar to superhuman AIs like AlphaZero. The approach is evaluated on twelve D4RL MuJoCo benchmark tasks and three target tracking tasks in a challenging, stochastic tokamak control simulator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning can help make better decisions and control systems without needing lots of new data. Normally, this type of learning uses static data to learn about the world and then makes decisions based on that understanding. However, sometimes there are many possible worlds that could behave in a similar way based on the available data. This paper presents a new way to deal with this uncertainty by treating offline reinforcement learning as a special kind of mathematical problem called a Bayes Adaptive Markov Decision Process (BAMDP). A new algorithm is introduced to solve BAMDPs, which can be used to improve decision-making and control systems in situations where there’s not enough data. |
Keywords
» Artificial intelligence » Reinforcement learning » Tracking
