Summary of Policy Mirror Descent with Lookahead, by Kimon Protopapas et al.
Policy Mirror Descent with Lookahead
by Kimon Protopapas, Anas Barakat
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 This paper presents Policy Mirror Descent (PMD), a framework that encompasses various policy gradient algorithms like natural policy gradient and connects with TRPO and PPO. PMD is a soft Policy Iteration algorithm implementing regularized 1-step greedy policy improvement. However, recent successes in reinforcement learning (RL) show that multi-step approaches outperform 1-step policies. The authors introduce h-PMD, incorporating multi-step greedy policy improvement with lookahead depth h. For solving discounted infinite horizon Markov Decision Processes with discount factor , the authors show that h-PMD enjoys a faster dimension-free ^h-linear convergence rate, contingent on computing multi-step greedy policies. An inexact version of h-PMD is proposed for estimating lookahead action values. Sample complexity results are established for both exact and inexact versions. The authors also extend the result to linear function approximation for scaling to large state spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new algorithm called Policy Mirror Descent (PMD) that helps machines learn how to make good decisions. PMD is special because it can be used with different types of learning algorithms. Usually, when computers learn from experience, they try to find the best action right away. But this new approach looks ahead and tries to find a better solution after considering multiple options. The researchers show that this way of thinking leads to faster learning and more accurate decisions. They also come up with ways to make the algorithm work better in situations where there are many possible outcomes. This could be useful for things like self-driving cars or medical diagnosis. |
Keywords
* Artificial intelligence * Reinforcement learning
