Summary of Improve Value Estimation Of Q Function and Reshape Reward with Monte Carlo Tree Search, by Jiamian Li
Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search
by Jiamian Li
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 The paper proposes a novel reinforcement learning algorithm for imperfect information games, focusing on Uno. It addresses challenges like Q value overestimation and reward sparsity by utilizing Monte Carlo Tree Search to average Q function estimates and reshape the reward structure. The algorithm is based on Double Deep Q Learning, but can be generalized for other algorithms requiring Q value estimation. Experiments show that it outperforms traditional methods like Double Deep Q Learning, Deep Monte Carlo, and Neural Fictitious Self Play, especially in games with multiple players. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to play Uno, a game where you don’t know what cards other players have. It’s hard for computers to learn how to play these kinds of games because they’re unpredictable. The researchers created an algorithm that can learn and improve by playing Uno. They tested it against other ways to play Uno and found that their method was better at winning, especially when there were more players. |
Keywords
* Artificial intelligence * Reinforcement learning
