Summary of Acquiring Diverse Skills Using Curriculum Reinforcement Learning with Mixture Of Experts, by Onur Celik et al.
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
by Onur Celik, Aleksandar Taranovic, Gerhard Neumann
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 proposes a new reinforcement learning (RL) method called Di-SkilL, which enables agents to learn diverse skills in various contexts. The approach uses Mixture of Experts, where each expert represents a skill as a contextual motion primitive. Di-SkilL optimizes each expert and its associated context distribution to a maximum entropy objective, encouraging the agent to learn different skills in similar contexts. This method also allows for automatic curricula learning, allowing each expert to focus on its best-performing sub-region of the context space. The paper demonstrates how energy-based models can be used to represent per-expert context distributions and efficiently train them using the standard policy gradient objective. The results show that Di-SkilL can learn diverse and performant skills in challenging robot simulation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Di-SkilL is a new way for robots and other machines to learn many different skills, like moving objects or navigating through spaces. Usually, these machines are only good at one skill, but with Di-SkilL, they can be trained to do lots of things. The method uses special “experts” that each represent a different skill, and it makes sure the experts work together by giving them goals to achieve. This helps the machine learn new skills more efficiently and automatically. The researchers tested Di-SkilL on robots doing tasks like picking up objects and showed that it can be very effective. |
Keywords
* Artificial intelligence * Mixture of experts * Reinforcement learning
