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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that
uses standard supervised learning methods as subroutines. Our goal is an algorithm that …
uses standard supervised learning methods as subroutines. Our goal is an algorithm that …
Revisiting fundamentals of experience replay
W Fedus, P Ramachandran… - International …, 2020 - proceedings.mlr.press
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but
there remain significant gaps in our understanding. We therefore present a systematic and …
there remain significant gaps in our understanding. We therefore present a systematic and …
When should we prefer offline reinforcement learning over behavioral cloning?
Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing
previously collected experience, without any online interaction. It is widely understood that …
previously collected experience, without any online interaction. It is widely understood that …
Datasets and benchmarks for offline safe reinforcement learning
This paper presents a comprehensive benchmarking suite tailored to offline safe
reinforcement learning (RL) challenges, aiming to foster progress in the development and …
reinforcement learning (RL) challenges, aiming to foster progress in the development and …