Summary of Dipper: Direct Preference Optimization to Accelerate Primitive-enabled Hierarchical Reinforcement Learning, by Utsav Singh et al.
DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning
by Utsav Singh, Souradip Chakraborty, Wesley A. Suttle, Brian M. Sadler, Vinay P Namboodiri, Amrit Singh Bedi
First submitted to arxiv on: 16 Jun 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 This paper introduces DIPPER, an efficient hierarchical approach for learning control policies in complex robotics tasks from human preference data. The method leverages direct preference optimization to learn a higher-level policy and reinforcement learning to learn a lower-level policy. DIPPER addresses challenges such as non-stationarity and infeasible subgoal generation by using primitive-informed regularization inspired by a novel bi-level optimization formulation of the hierarchical reinforcement learning problem. To validate the approach, extensive experimental analysis is performed on various challenging robotics tasks, demonstrating that DIPPER outperforms hierarchical and non-hierarchical baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots learn new skills by using human preferences to guide them. It’s like teaching a robot how to do a tricky task by showing it what you want the outcome to be. The researchers developed a new way of learning called DIPPER, which is more efficient and effective than previous methods. They tested it on different robotics tasks and found that it worked better than other approaches. |
Keywords
* Artificial intelligence * Optimization * Regularization * Reinforcement learning
