Summary of Improving Deep Reinforcement Learning by Reducing the Chain Effect Of Value and Policy Churn, By Hongyao Tang and Glen Berseth
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
by Hongyao Tang, Glen Berseth
First submitted to arxiv on: 7 Sep 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 This paper explores the challenges posed by deep neural networks in Reinforcement Learning (RL), particularly the non-stationary nature of training. Specifically, it delves into the phenomenon of “churn” in function approximation, which can lead to uncontrolled changes and bias in learning dynamics. The authors characterize churn through Generalized Policy Iteration and discover a chain effect that compounds and biases the learning process. They then concretize the study by investigating the impact of churn on different RL settings, including greedy action deviation, trust region violation, and dual bias of policy value. To mitigate these issues, the paper proposes Churn Approximated ReductIoN (CHAIN), a method that can be easily integrated into existing DRL algorithms. Experimental results demonstrate the effectiveness of CHAIN in reducing churn and improving learning performance across various RL settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can learn from experiences. It’s trying to solve a problem where AI gets stuck or makes mistakes because it’s not learning correctly. The authors found that this happens because the AI is making small changes each time it learns, which can add up and make the learning process worse. They looked at different ways this can happen in AI systems and proposed a solution called CHAIN to fix these issues. This means AI will be able to learn better and make fewer mistakes. |
Keywords
» Artificial intelligence » Reinforcement learning
