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Augmented World Models Facilitate Zero-Shot Dynamics Generalization From A Single Offline Environment
Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or …
Philip J. Ball
,
Cong Lu
,
Jack Parker-Holder
,
Stephen Roberts
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arXiv
Website
PDF
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
To rapidly learn a new task, it is often essential for agents to explore efficiently – especially when performance matters from …
Luisa Zintgraf
,
Leo Feng
,
Cong Lu
,
Maximilian Igl
,
Kristian Hartikainen
,
Katja Hofmann
,
Shimon Whiteson
Cite
arXiv
PDF
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has proved highly successful in improving the sample efficiency of …
Tim G. J. Rudner
,
Cong Lu
,
Michael A. Osborne
,
Yarin Gal
,
Yee Whye Teh
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Website
PDF
Revisiting Design Choices in Offline Model Based Reinforcement Learning
Offline reinforcement learning enables agents to make use of large pre-collected datasets of environment transitions and learn control …
Cong Lu
,
Philip J. Ball
,
Jack Parker-Holder
,
Michael A. Osborne
,
Stephen Roberts
Cite
PDF
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian …
Xingchen Wan
,
Vu Nguyen
,
Huong Ha
,
Binxin Ru
,
Cong Lu
,
Michael A. Osborne
Cite
arXiv
PDF
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
Trading off exploration and exploitation in an unknown environment is key to maximising expected online return during learning. A …
Luisa Zintgraf
,
Sebastian Schulze
,
Cong Lu
,
Leo Feng
,
Maximilian Igl
,
Kyriacos Shiarlis
,
Yarin Gal
,
Katja Hofmann
,
Shimon Whiteson
Cite
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