Summary of Stochastic Q-learning For Large Discrete Action Spaces, by Fares Fourati et al.
Stochastic Q-learning for Large Discrete Action Spaces
by Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF); Robotics (cs.RO); Machine Learning (stat.ML)
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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 novel stochastic value-based reinforcement learning (RL) approaches to address the computational burden of traditional value-based RL methods like Q-learning. In complex environments with large discrete action spaces, these approaches efficiently update value functions and select actions by considering only a variable stochastic set of sublinear-sized actions, potentially as small as O(log(n)). The authors propose several stochastic value-based RL methods, including Stochastic Q-learning, StochDQN, and StochDDQN. They establish the theoretical convergence of Stochastic Q-learning and provide an analysis of stochastic maximization. Empirical validation shows that these approaches outperform baseline methods across diverse environments, achieving near-optimal average returns in significantly reduced time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make machines learn from mistakes by solving a big problem called reinforcement learning. They want to help computers decide what to do next when there are many options. Current methods work well but use too much computer power and can’t handle really big problems. The researchers came up with new ideas that let the computer consider only some of the possible actions, not all of them. This makes it faster and more efficient. They tested their ideas on different tasks and found they worked better than old ways. |
Keywords
» Artificial intelligence » Reinforcement learning
