Summary of Towards Global Optimality For Practical Average Reward Reinforcement Learning Without Mixing Time Oracles, by Bhrij Patel et al.
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
by Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Amrit Singh Bedi, Dinesh Manocha
First submitted to arxiv on: 18 Mar 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 the realm of reinforcement learning, ensuring global convergence is a significant challenge when dealing with average-reward Markov Decision Processes (MDPs). A crucial requirement is knowing the mixing time, which measures how long a chain needs to achieve its stationary distribution under a fixed policy. However, estimating this in large state spaces can be daunting and impractical, making gradient estimation challenging. To overcome this limitation, we introduce the Multi-level Actor-Critic (MAC) framework, incorporating the Multi-level Monte-Carlo (MLMC) gradient estimator. Our approach eliminates the dependency on mixing time knowledge, achieving global convergence for average-reward MDPs. Furthermore, our method exhibits a tight dependence of O(sqrt(tau_mix)), which is the best known from prior work. We demonstrate MAC’s superiority over existing policy gradient-based methods in a 2D grid world navigation experiment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for machines to learn by making decisions and getting rewards or penalties. In this field, it’s hard to get the best results because we need to know how long it takes for some processes to settle down. This makes it difficult to train machines quickly enough to make good choices. Our new approach, called Multi-level Actor-Critic (MAC), solves this problem by not requiring us to know when these processes settle down. MAC is better than other methods in this area and can be used in situations where we need machines to learn quickly. |
Keywords
* Artificial intelligence * Reinforcement learning
