Summary of Dense Dynamics-aware Reward Synthesis: Integrating Prior Experience with Demonstrations, by Cevahir Koprulu et al.
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
by Cevahir Koprulu, Po-han Li, Tianyu Qiu, Ruihan Zhao, Tyler Westenbroek, David Fridovich-Keil, Sandeep Chinchali, Ufuk Topcu
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed framework accelerates online reinforcement learning by synthesizing dense rewards using a combination of task-agnostic prior data and task-specific expert demonstrations. The method leverages these priors to shape rewards that are aware of the task dynamics, leading to faster learning in sparse-reward environments. Experiments demonstrate the effectiveness of this approach in guiding online learning agents to achieve faraway goals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a way to help machines learn new tasks more quickly. It combines information from two sources: a general idea of what actions are useful (prior data set), and specific examples of good actions for a particular task (expert demonstrations). This combination is used to create rewards that consider the dynamics of the task, making it easier for online learning agents to achieve their goals. |
Keywords
» Artificial intelligence » Online learning » Reinforcement learning
