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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
In this tutorial article, we aim to provide the reader with the conceptu...
05/04/2020 ∙ by Sergey Levine, et al. ∙
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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙
41
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Datasets for Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙
6
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Learning To Reach Goals Without Reinforcement Learning
Imitation learning algorithms provide a simple and straightforward appro...
12/12/2019 ∙ by Dibya Ghosh, et al. ∙
0
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When to Trust Your Model: Model-Based Policy Optimization
Designing effective model-based reinforcement learning algorithms is dif...
06/19/2019 ∙ by Michael Janner, et al. ∙
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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Off-policy reinforcement learning aims to leverage experience collected ...
06/03/2019 ∙ by Aviral Kumar, et al. ∙
7
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Diagnosing Bottlenecks in Deep Q-learning Algorithms
Q-learning methods represent a commonly used class of algorithms in rein...
02/26/2019 ∙ by Justin Fu, et al. ∙
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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Reinforcement learning is a promising framework for solving control prob...
02/20/2019 ∙ by Justin Fu, et al. ∙
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Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition
The design of a reward function often poses a major practical challenge ...
05/29/2018 ∙ by Justin Fu, et al. ∙
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Generalizing Skills with Semi-Supervised Reinforcement Learning
Deep reinforcement learning (RL) can acquire complex behaviors from low-...
12/01/2016 ∙ by Chelsea Finn, et al. ∙
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