Summary of Policy Gradient Methods For Risk-sensitive Distributional Reinforcement Learning with Provable Convergence, by Minheng Xiao et al.
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
by Minheng Xiao, Xian Yu, Lei Ying
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 this paper, researchers tackle the challenge of developing reliable reinforcement learning (RL) methods for high-stakes applications by introducing a novel policy gradient approach for distributional RL with general coherent risk measures. The proposed method, categorical distributional policy gradient algorithm (CDPG), approximates any distribution using a categorical family supported on fixed points. This work provides an analytical form of the probability measure’s gradient for any distribution and offers guarantees for finite-support optimality and finite-iteration convergence under inexact policy evaluation and gradient estimation. Experiments on stochastic Cliffwalk and CartPole environments demonstrate the benefits of considering risk-sensitive settings in DRL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure that machines can learn to make good choices even when things might go wrong. It’s like playing a game where you don’t want to lose too much money. The researchers came up with a new way for machines to learn how to play games like this by thinking about the chance of losing or gaining something. They tested it on two kinds of games and showed that it works better than other ways of doing things. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
