Summary of Deep Exploration with Pac-bayes, by Bahareh Tasdighi et al.
Deep Exploration with PAC-Bayes
by Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 tackles the challenge of reinforcement learning for continuous control under delayed rewards, a problem that has significant real-life implications. The existing deep exploration methods are designed for small discrete action spaces and their generalizability to state-of-the-art continuous control remains unproven. To address this, the authors propose a novel PAC-Bayesian actor-critic algorithm, named PBAC, which quantifies the error of the Bellman operator through a PAC-Bayes bound. The algorithm trains an ensemble of critic networks and uses them to derive an objective function for training the soft actor network. The agent performs deep exploration by acting epsilon-greedily on a randomly chosen actor head. The authors demonstrate the effectiveness of PBAC in consistently discovering delayed rewards on a diverse set of continuous control tasks with varying difficulty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping machines learn new skills, like how to walk or stand up, even when they don’t get immediate rewards for doing so. Right now, there’s no good way to do this, especially if the actions are continuous (like a robot arm moving). The authors came up with a new approach called PBAC that uses a special kind of math called PAC-Bayesian to help the machine learn. They tested it on lots of different tasks and showed that it works really well. |
Keywords
* Artificial intelligence * Objective function * Reinforcement learning
