Summary of A Finite-sample Analysis Of An Actor-critic Algorithm For Mean-variance Optimization in a Discounted Mdp, by Tejaram Sangadi et al.
A Finite-Sample Analysis of an Actor-Critic Algorithm for Mean-Variance Optimization in a Discounted MDP
by Tejaram Sangadi, L. A. Prashanth, Krishna Jagannathan
First submitted to arxiv on: 12 Jun 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 In this paper, researchers investigate mean-variance optimization in discounted reward Markov Decision Processes (MDPs) to advance applications in risk-sensitive reinforcement learning. The study focuses on Temporal Difference (TD) learning algorithms with linear function approximation (LFA) for policy evaluation, deriving finite-sample bounds that hold in both the mean-squared sense and with high probability under tail iterate averaging, with and without regularization. The results show an exponentially decaying dependence on the initial error and a convergence rate of O(1/t) after t iterations. Additionally, the authors integrate Simultaneous Perturbation Stochastic Approximation (SPSA)-based actor updates with LFA critics, establishing an O(n^{-1/4}) convergence guarantee. These findings provide theoretical guarantees for risk-sensitive actor-critic methods in reinforcement learning, highlighting variance as a key risk measure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are trying to make computers learn better when there’s uncertainty involved. They’re looking at ways to balance the rewards from doing things right with the risks of not doing them well enough. The team developed new algorithms that can help machines learn faster and more accurately in situations where the outcome is uncertain. This could have big implications for things like self-driving cars or robots that need to make decisions quickly. |
Keywords
» Artificial intelligence » Optimization » Probability » Regularization » Reinforcement learning
