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Understanding The World Through Action, Sergey Levine, UC Berkeley, 2021
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Off-Policy Deep Reinforcement Learning without Exploration, S. Fujimoto et al, 2019
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Weakly-Supervised Reinforcement Learning for Controllable Behavior, L. Lee et al, CMU, NeurIPS, 2020
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Hierarchical Relative Enthropy Policy Search, Christian Daniel et al, TU Darmstadt, 2000
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Offline Meta-Reinforcement Learning with Online Self-Supervision, V. Pong et al, UC Berkeley, 2022
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Tree-Based Batch Mode Reinforcement Learning, Damien Ernst, Pierre Guerts, Louis Wehenkel, 2005
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Decisions from Data: How Offline Reinforcement Learning Will Change How We Use Machine Learning, Sergey Levine, 2020
related paper: D4RL: Datasets for Deep Data-Driven Reinforcement Learning, Justin Fu et al, UC Berkeley, 2021
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The Monster of Distributional Shift in Offline Reinforcement Learning and How to Pacify it
related paper: Generalization in Humans and Deep Neural Networks, R. Geirhos et al, UoTubingen, 2020
related paper: A Survey on Offline Reinforcement Learning: Taxonomy, Review and Open Problems, RF Prudencio et al, 2023
related paper: When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?, Aviral Kumar et al, UC Berkeley, Google Research, 2022
related paper: Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction, Aviral Kumar et al, UC Berkeley, Google Brain, 2019
related paper: Off-Policy Deep Reinforcement Learning without Exploration, S. Fujimoto et al, 2019
related paper: Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog, N. Jaques et al, MIT, 2019
related paper: Relative Entropy Policy Search, Jan Peters et al, Max Planck Institute, 2010
related paper: Awac: accelerating online reinforcement learning with offline datasets, A. Nair, UC Berkeley, 2020
related paper: Advantage-weighted regression: simple and scalable off-policy reinforcement learning, XB Peng et al, UC Berkeley, 2019
related paper: Offline reinforcement learning with implicit q-learning, I. Kostrikov et al, UC Berkeley, 2021
related paper: Conservative q-learning for offline reinforcement learning, Aviral Kumar et al, UC Berkeley, 2020
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Curious Agents: An Introduction with Dries Smith
https://medium.com/@dries.epos/curious-agents-ebfee02ef024
(code of this series: https://github.com/DriesSmit/CuriousAgents)
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Curious Agents II: Solving MountainCar without Rewards
https://medium.com/@dries.epos/curious-agents-ii-solving-mountaincar-without-rewards-c49ae2177819
(code of this series: https://github.com/DriesSmit/CuriousAgents)
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Curious Agents III: BYOL-Explore
https://medium.com/@dries.epos/curious-agents-iii-byol-explore-93f34fa6146a
(code of this series: https://github.com/DriesSmit/CuriousAgents)
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Curious Agents IV: BYOL-Hindsight
https://medium.com/@dries.epos/curious-agents-iv-byol-hindsight-318c559175f0
(code of this series: https://github.com/DriesSmit/CuriousAgents)
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Understanding the World Through Action: RL as a Foundation for Scalable Self-Supervised Learning with Sergey Levine
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How Robots Can Learn End-to-End from Data with Sergey Levine
https://medium.com/@sergey.levine/how-robots-can-learn-end-to-end-from-data-3d879b0a2ba1
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Self-Supervised Learning Using Projection Heads: Boost performance with unlabeled data with Daniel Warfield
https://towardsdatascience.com/self-supervised-learning-using-projection-heads-b77af3911d33