Summary of Ancestral Reinforcement Learning: Unifying Zeroth-order Optimization and Genetic Algorithms For Reinforcement Learning, by So Nakashima and Tetsuya J. Kobayashi
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
by So Nakashima, Tetsuya J. Kobayashi
First submitted to arxiv on: 18 Aug 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 This paper presents Ancestral Reinforcement Learning (ARL), a novel approach that combines Zeroth-Order Optimization (ZOO) and Genetic Algorithms (GA) to enhance the performance and applicability of Reinforcement Learning (RL). ARL leverages an agent population to estimate the gradient of the objective function, enabling robust policy refinement even in non-differentiable scenarios. The key idea is that each agent within a population infers gradient by exploiting the history of its ancestors, while maintaining policy diversity through mutation and selection. This approach implicitly induces KL-regularization of the objective function, leading to enhanced exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to learn from trying different actions in unknown situations. It combines two existing methods, Zeroth-Order Optimization and Genetic Algorithms, to make it easier to find the best solution. The new method, Ancestral Reinforcement Learning, uses a group of agents that share information about what worked well in the past to decide what to do next. This helps explore more possibilities and avoid getting stuck in one strategy. |
Keywords
» Artificial intelligence » Objective function » Optimization » Regularization » Reinforcement learning
