Summary of Pg-rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods, by Woojae Jeon et al.
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods
by WooJae Jeon, KangJun Lee, Jeewoo Lee
First submitted to arxiv on: 18 Jul 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 PG-Rainbow is a novel algorithm that combines distributional reinforcement learning with policy gradients to improve sample efficiency and decision-making. The algorithm addresses limitations in existing policy gradient methods by incorporating reward distribution information into the critic network of Proximal Policy Optimization. This leads to more sophisticated decision-making processes, as demonstrated through experiments in the Atari-2600 game suite using the Arcade Learning Environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PG-Rainbow is a new way to help computers learn and make decisions. It uses two existing methods to improve how it chooses what actions to take next. The result is better choices that consider different outcomes. This can be useful for many applications, like playing games or controlling robots. The algorithm was tested in a series of games and showed promising results. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
