Summary of More Efficient Randomized Exploration For Reinforcement Learning Via Approximate Sampling, by Haque Ishfaq et al.
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
by Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
First submitted to arxiv on: 18 Jun 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 This paper proposes a novel algorithmic framework for reinforcement learning (RL) exploration, combining Feel-Good Thompson Sampling (FGTS) and approximate sampling methods. The framework leverages recent advances in FGTS, originally considered computationally intractable, to achieve improved regret bounds in linear Markov Decision Processes (MDPs). By incorporating different samplers, such as Langevin Monte Carlo, the algorithm yields a dependency on dimensionality that surpasses existing randomized algorithms. Empirically, the framework outperforms strong baselines on challenging games from the Atari 57 suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about how machines make decisions when trying new things. It’s like teaching a robot to explore and find the best way to play a game. The researchers created a special algorithm that combines two ideas: “Feel-Good Thompson Sampling” (FGTS) and some clever ways of choosing which actions to take next. This helps the algorithm do better in games where you need to try lots of things before winning. It’s like getting a prize for taking risks! |
Keywords
* Artificial intelligence * Reinforcement learning
