Summary of A Simple Mixture Policy Parameterization For Improving Sample Efficiency Of Cvar Optimization, by Yudong Luo et al.
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization
by Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 A reinforcement learning algorithm utilizing policy gradients (PG) to optimize Conditional Value at Risk (CVaR) faces challenges with sample inefficiency, hindering practical applications. The proposed solution is a simple mixture policy parameterization that integrates a risk-neutral policy with an adjustable policy to form a risk-averse policy. This approach utilizes all collected trajectories for policy updating and prevents gradient vanishing by stimulating higher returns through the risk-neutral component. Experimental results show this method excels in identifying risk-averse CVaR policies across various Mujoco environments, outperforming traditional CVaR-PG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make a type of artificial intelligence learn better is proposed. This AI uses a technique called policy gradients (PG) to optimize something called Conditional Value at Risk (CVaR). The problem with this method is that it doesn’t use all the data it has, which makes it not very good in real-life situations. To fix this, researchers came up with a simple way to combine two types of policies together. This new approach uses all the data and prevents some common problems that can make the AI learn slowly or incorrectly. The results show that this new method works better than the old one in many different scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
