Summary of Provable Risk-sensitive Distributional Reinforcement Learning with General Function Approximation, by Yu Chen et al.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
by Yu Chen, Xiangcheng Zhang, Siwei Wang, Longbo Huang
First submitted to arxiv on: 28 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 In reinforcement learning (RL), risk management is vital when making decisions under uncertainty. This paper introduces Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), a general framework that accounts for risk using static Lipschitz Risk Measures (LRM) and general function approximation. The framework enables analysis of the impact of estimation functions on RSRL strategies’ effectiveness and sample complexity evaluation. Two meta-algorithms are designed: RS-DisRL-M, a model-based strategy, and RS-DisRL-V, a model-free approach for value function approximation. Novel estimation techniques using Least Squares Regression (LSR) and Maximum Likelihood Estimation (MLE) in distributional RL with augmented Markov Decision Process (MDP) are also presented. This marks a significant contribution to statistically efficient algorithms in risk-sensitive RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better decisions when we’re not sure what will happen next. It’s like trying to navigate a maze while avoiding danger zones. The paper introduces a new way to think about this problem called Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL). This approach helps us make safer choices by considering the possibility of bad outcomes. Two new techniques are developed: one for making decisions based on a model, and another that doesn’t need a model at all. The paper also shows how to use these techniques to improve decision-making. Overall, it’s an important step forward in helping machines learn to make better choices. |
Keywords
* Artificial intelligence * Likelihood * Regression * Reinforcement learning
