[1901.10995] Go-Explore: a New Approach for Hard-Exploration Problems
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pi…
\nobibliography * Go-Explore: a New Approach for Hard-Exploration Problems Adrien Ecoffet Joost Huizinga Joel Lehman Kenneth O. Stanley* Jeff Clune* Uber AI Labs San Francisco, CA 94103 adrienecoffet,joost.hui,jclune@gmail.com *Co-senior authors Abstract A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method
related reading
- go-explore-nature.pdfadrien.ecoffet.com
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- Exploration Strategies in Deep Reinforcement Learning | Lil'Loglilianweng.github.io
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- Exploration for the Efficient Deployment of Reinforcement Learning Agentsopenreview.net
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- How to Explore to Scale RL Training of LLMs on Hard Problems? – Machine Learning Blog | ML@CMU | Carnegie Mellon Universityblog.ml.cmu.edu
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