Summary of Linear Function Approximation As a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges, by Hari Srikanth
Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
by Hari Srikanth
First submitted to arxiv on: 27 May 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 explores the effectiveness of Neural Network-based approximations of the Value function in Policy-Based methods, specifically Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While these methods excel in complex environments, they may not be necessary in simpler settings where a computationally less-expensive Value approximation method could suffice. The authors present an implementation of Natural Actor Critic algorithms with actor updates through Natural Policy Gradient methods, arguing that this approach can outperform TRPO and PPO in low-dimensional problems. They demonstrate the superiority of their algorithm over these complex neural network architectures on Reinforcement Learning benchmarks Cart Pole and Acrobot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers learn to make decisions by trying different actions in a situation. It compares two ways that computers use to decide what action is best: using really smart computer programs called Neural Networks, or using simpler methods. The scientists found that for problems that aren’t too hard, the simpler methods work just as well and are much faster. They tested their ideas on two games, Cart Pole and Acrobot, and showed that their simple method was better than the more complicated one. |
Keywords
* Artificial intelligence * Neural network * Optimization * Reinforcement learning
