Summary of Two-timescale Critic-actor For Average Reward Mdps with Function Approximation, by Prashansa Panda and Shalabh Bhatnagar
Two-Timescale Critic-Actor for Average Reward MDPs with Function Approximation
by Prashansa Panda, Shalabh Bhatnagar
First submitted to arxiv on: 2 Feb 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 Recent advances in Actor-Critic (AC) algorithms have led to non-asymptotic convergence analyses for several works. A two-timescale critic-actor algorithm was presented for discounted cost settings with reversed actor-critic timescales, but only asymptotic convergence was shown. Our work addresses this limitation by proposing a novel two-timescale critic-actor algorithm with function approximation in the long-run average reward setting. We present finite-time and asymptotic convergence analyses, demonstrating that our scheme achieves a sample complexity of (^{-(2+)}) for mean squared error upper bounded by . Our analysis also shows almost sure asymptotic convergence of the critic recursion to an attractor differential inclusion with actor parameters corresponding to local maxima of a perturbed average reward objective. We validate our findings through numerical experiments on three benchmark settings, where our critic-actor algorithm outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of algorithm called Actor-Critic has been studied recently. This algorithm helps make decisions in situations where there are many possible choices. Some previous studies have looked at how well this algorithm works when it’s used over a long period of time, but they haven’t shown how it performs in the short term. Our study addresses this by creating a new type of Actor-Critic algorithm that can be used for both short-term and long-term decisions. We’ve also developed a way to analyze how well this algorithm works, which shows that it can make good decisions quickly and accurately. To test our findings, we ran computer simulations using three different scenarios, and the results showed that our algorithm performed better than other similar algorithms. |
