Summary of Hierarchical Average-reward Linearly-solvable Markov Decision Processes, by Guillermo Infante et al.
Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
by Guillermo Infante, Anders Jonsson, Vicenç Gómez
First submitted to arxiv on: 9 Jul 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 The proposed hierarchical reinforcement learning approach for Linearly-solvable Markov Decision Processes (LMDPs) enables simultaneous learning of low-level and high-level tasks without restricting the complexity of the former. By partitioning the state space into smaller subtasks, the method leverages compositionality to exactly represent the value function of the high-level task. This novel approach outperforms flat average-reward reinforcement learning by one or several orders of magnitude in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of teaching machines to make decisions is developed. Instead of focusing on simple tasks, this method can learn both simple and complex tasks at the same time. It does this by breaking down the task into smaller parts that are easier to solve. The key idea is that the solutions to these small problems can be combined to find a solution to the larger problem. This approach works much better than previous methods in certain situations. |
Keywords
* Artificial intelligence * Reinforcement learning
