Summary of A Reductions Approach to Risk-sensitive Reinforcement Learning with Optimized Certainty Equivalents, by Kaiwen Wang et al.
A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents
by Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 The paper proposes two meta-algorithms for risk-sensitive reinforcement learning (RL) that optimize various risk measures, including conditional value-at-risk (CVaR), entropic risk, and mean-variance. The algorithms leverage existing risk-neutral RL methods in an augmented Markov Decision Process (MDP). The optimism-based algorithm establishes novel bounds in complex MDPs, while the gradient-based algorithm provides guarantees for monotone improvement and global convergence under a discrete reward assumption. The paper demonstrates the effectiveness of these algorithms by learning the optimal history-dependent policy in a proof-of-concept MDP, where all Markovian policies fail. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to learn how to make good decisions when there’s uncertainty or risk involved. It develops new ways for machines to choose between different options based on how likely they are to work out well. The goal is to find the best way to get a reward while minimizing the chance of getting a bad outcome. The researchers show that their methods can be used in complex situations where all the usual rules don’t apply. |
Keywords
* Artificial intelligence * Reinforcement learning
