Summary of Model-based Rl As a Minimalist Approach to Horizon-free and Second-order Bounds, by Zhiyong Wang et al.
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
by Zhiyong Wang, Dongruo Zhou, John C.S. Lui, Wen Sun
First submitted to arxiv on: 16 Aug 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 A simple Model-based Reinforcement Learning (RL) framework, which involves learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model, achieves strong regret and sample complexity bounds in online and offline RL settings. The framework, equipped with optimistic and pessimistic planning procedures, obtains nearly horizon-free and second-order bounds under certain conditions. Specifically, it achieves polynomial bounds that scale only with respect to the variances of the returns of the policies, which can be small when the system is nearly deterministic or the optimal policy has low values. The algorithms are simple, standard, and widely studied in the RL literature, relying on MLE model learning, version space construction, and optimistic or pessimistic planning. They do not require specialized designs, such as variance learning, and can leverage non-linear function approximations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of using computers to learn from trial-and-error experiences is being explored. This method, called Model-based Reinforcement Learning (RL), helps machines make better decisions by learning from their mistakes. Researchers have developed a simple framework for RL that works well in different situations. This framework involves learning how the world responds to different actions and then planning what to do next. It’s like having a map of the environment and using it to decide where to go next. The scientists showed that this approach can make good decisions quickly, even when the situation is complex or uncertain. |
Keywords
» Artificial intelligence » Likelihood » Reinforcement learning
