Summary of Composing Reinforcement Learning Policies, with Formal Guarantees, by Florent Delgrange et al.
Composing Reinforcement Learning Policies, with Formal Guarantees
by Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann Nowé, Guillermo A. Pérez
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research proposes a novel framework for controller design in complex environments, specifically those with a two-level structure comprising high-level graphs and lower-level Markov decision processes, or “rooms”. The framework utilises reactive synthesis for high-level tasks and reinforcement learning for training low-level policies. A key innovation is the omission of model distillation steps, allowing for more efficient policy training. The authors also provide formal guarantees on policy performance and abstraction quality, which are essential advantages of their approach. This framework demonstrates scalability and reusability of low-level policies in challenging case studies involving moving obstacles and visual inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a new way to design controllers for complex environments. These environments have two parts: a high-level map and many smaller “rooms” that follow rules called Markov decision processes. The team used two different approaches to solve these problems. One method is reactive synthesis, which helps with high-level tasks, while another is reinforcement learning, which trains low-level policies. What’s unique about this framework is that it skips a step usually needed in policy training. This makes the process more efficient. Additionally, the researchers provide mathematical proof of how well their approach works and ensures its quality. They tested this method in difficult scenarios where an agent must navigate through environments with moving obstacles and visual inputs. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
