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SCOTT FUJIMOTO
Browse, search & ask about the research work by "TIJMEN TIELEMAN"
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Selected work | Use "Search" to find all #paper(s): 31
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Paper
Addressing Function Approximation Error in Actor-Critic Methods
IF:9
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: We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.
Scott Fujimoto
;
Herke Hoof
;
David Meger
;
icml
2018-07-10
Paper
Addressing Function Approximation Error In Actor-Critic Methods
IF:9
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: We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.
Scott Fujimoto
;
Herke van Hoof
;
David Meger
;
arxiv-cs.AI
2018-02-26
Paper
Off-Policy Deep Reinforcement Learning Without Exploration
IF:8
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: In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting.
Scott Fujimoto
;
David Meger
;
Doina Precup
;
arxiv-cs.LG
2018-12-06
Paper
A Minimalist Approach to Offline Reinforcement Learning
IF:7
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: In this paper we aim to make a deep RL algorithm work while making minimal changes.
Scott Fujimoto
;
Shixiang (Shane) Gu
;
nips
2021-11-20
Paper
A Minimalist Approach to Offline Reinforcement Learning
IF:7
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: In this paper we aim to make a deep RL algorithm work while making minimal changes.
Scott Fujimoto
;
Shixiang Shane Gu
;
arxiv-cs.LG
2021-06-12
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