Summary of Recursive Backwards Q-learning in Deterministic Environments, by Jan Diekhoff et al.
Recursive Backwards Q-Learning in Deterministic Environments
by Jan Diekhoff, Jörn Fischer
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 an innovative approach to reinforcement learning, building upon the success of Q-learning in solving stochastic problems. The authors introduce the recursive backwards Q-learning (RBQL) agent, which combines exploration and model-building with backward propagation to efficiently solve deterministic problems. By leveraging a model of the environment, RBQL outperforms traditional Q-learning methods in tasks like finding the shortest path through a maze. This work has implications for various applications, such as robotics and game playing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn faster and better. Right now, they’re really good at solving problems when things are unpredictable, but they struggle with problems where everything follows a clear rule. To fix this, the researchers created a new way to train AI agents called recursive backwards Q-learning (RBQL). It’s like having a map of the environment that helps the agent find the best solution quickly and efficiently. This is important because it can be used in real-world applications like robots navigating through spaces or playing games. |
Keywords
» Artificial intelligence » Reinforcement learning
