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pro vyhledávání: '"Gehring, Clement"'
A natural approach for reinforcement learning is to predict future rewards by unrolling a neural network world model, and to backpropagate through the resulting computational graph to learn a policy. However, this method often becomes impractical for
Externí odkaz:
http://arxiv.org/abs/2402.05290
Autor:
Ni, Tianwei, Eysenbach, Benjamin, Seyedsalehi, Erfan, Ma, Michel, Gehring, Clement, Mahajan, Aditya, Bacon, Pierre-Luc
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks hav
Externí odkaz:
http://arxiv.org/abs/2401.08898
Autor:
Fathi, Mahan, Gehring, Clement, Pilault, Jonathan, Kanaa, David, Bacon, Pierre-Luc, Goroshin, Ross
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theoretically, such features can be used to simplify many problems in modeling and control of NLDS. In this work we
Externí odkaz:
http://arxiv.org/abs/2310.15386
Autor:
Gehring, Clement
Reinforcement learning (RL) provides a general framework for data-driven decision making. However, the very same generality that makes this approach applicable to a wide range of problems is also responsible for its well-known inefficiencies. In this
Externí odkaz:
https://hdl.handle.net/1721.1/144562
Autor:
Gehring, Clement, Asai, Masataro, Chitnis, Rohan, Silver, Tom, Kaelbling, Leslie Pack, Sohrabi, Shirin, Katz, Michael
Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based problems found in class
Externí odkaz:
http://arxiv.org/abs/2109.14830
Publikováno v:
In Applied Energy 15 August 2024 368
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as
Externí odkaz:
http://arxiv.org/abs/1706.01445
Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update the value function, with a linear time complexity in the number of fea
Externí odkaz:
http://arxiv.org/abs/1511.08495
Deep reinforcement learning (RL) methods provide state-of-art performance in complex control tasks. However, it has been widely recognized that RL methods often fail to generalize due to unaccounted uncertainties. In this work, we propose a game theo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________38::d45c8771643502dd429930b2bba01788
https://resolver.caltech.edu/CaltechAUTHORS:20210727-172214672
https://resolver.caltech.edu/CaltechAUTHORS:20210727-172214672