Summary of Inductive Generalization in Reinforcement Learning From Specifications, by Vignesh Subramanian et al.
Inductive Generalization in Reinforcement Learning from Specifications
by Vignesh Subramanian, Rohit Kushwah, Subhajit Roy, Suguman Bansal
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 novel inductive generalization framework presented in this paper enables reinforcement learning (RL) from logical specifications. The framework leverages the natural inductive structure found in many interesting RL tasks, which have similar overarching goals but differ inductively in low-level predicates and distributions. The proposed approach uses a higher-order function, a policy generator, to generate adapted policies for instances of an inductive task in a zero-shot manner. This framework is evaluated on challenging control benchmarks, demonstrating its promise in generalizing to unseen policies for long-horizon tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn from logical specifications in reinforcement learning (RL). It’s like teaching a robot to do new things without showing it exactly how to do them. The approach works by finding patterns between similar RL tasks and using that information to create new policies. This means the robot can learn to do new things on its own, without needing to be shown every step. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Zero shot
