Summary of Efficient Model-based Reinforcement Learning Through Optimistic Thompson Sampling, by Jasmine Bayrooti et al.
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
by Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 In this research paper, the authors propose a novel approach to principled exploration for complex robot behavior, specifically focusing on optimistic exploration strategies that prioritize exploring high-reward regions of the state-action space. The proposed method, based on Thompson sampling, addresses the limitation of existing methods by incorporating belief updates about reward and state connections. Experimental results on MuJoCo and VMAS continuous control tasks demonstrate the effectiveness of the approach in accelerating learning in environments with sparse rewards, action penalties, and difficult-to-explore regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores new ways to help robots learn complex behaviors through interactions with their environment. The authors want to find better methods for exploring the huge space of possible actions and states that a robot can be in. They propose a new approach called optimistic exploration, which is like taking calculated risks to find the best rewards. The authors test this approach on some challenging control tasks and show that it helps robots learn much faster. |
