Summary of Pid Accelerated Temporal Difference Algorithms, by Mark Bedaywi et al.
PID Accelerated Temporal Difference Algorithms
by Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC); 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 In this paper, researchers tackle the challenge of long-horizon tasks in reinforcement learning (RL), where conventional algorithms like Value Iteration and Temporal Difference (TD) learning struggle with slow convergence. Building on the PID VI algorithm that leverages control theory ideas to accelerate Value Iteration, the authors introduce PID TD Learning and PID Q-Learning algorithms for RL scenarios where only environmental samples are available. The paper provides theoretical analysis of PID TD Learning’s convergence rate and its acceleration over conventional TD Learning. Additionally, a method is proposed for adapting PID gains in noisy environments, which is empirically verified. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For long-horizon tasks, most reinforcement learning (RL) algorithms struggle to converge efficiently. The authors introduce two new algorithms, PID TD Learning and PID Q-Learning, that can help solve this problem. These algorithms use ideas from control theory to speed up the learning process. In this paper, the authors also provide a detailed analysis of how well these algorithms work and how they compare to traditional methods. |
Keywords
* Artificial intelligence * Reinforcement learning
