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pro vyhledávání: '"Taïga, Adrien Ali"'
Autor:
Farebrother, Jesse, Orbay, Jordi, Vuong, Quan, Taïga, Adrien Ali, Chebotar, Yevgen, Xiao, Ted, Irpan, Alex, Levine, Sergey, Castro, Pablo Samuel, Faust, Aleksandra, Kumar, Aviral, Agarwal, Rishabh
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-ba
Externí odkaz:
http://arxiv.org/abs/2403.03950
Publikováno v:
Published as a conference paper at ICLR 2020
Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which ex
Externí odkaz:
http://arxiv.org/abs/2109.11052
Despite the wealth of research into provably efficient reinforcement learning algorithms, most works focus on tabular representation and thus struggle to handle exponentially or infinitely large state-action spaces. In this paper, we consider episodi
Externí odkaz:
http://arxiv.org/abs/2003.04069
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fix
Externí odkaz:
http://arxiv.org/abs/1908.02388
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To
Externí odkaz:
http://arxiv.org/abs/1901.11524
Autor:
Bellemare, Marc G., Dabney, Will, Dadashi, Robert, Taiga, Adrien Ali, Castro, Pablo Samuel, Roux, Nicolas Le, Schuurmans, Dale, Lattimore, Tor, Lyle, Clare
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as au
Externí odkaz:
http://arxiv.org/abs/1901.11530
Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate expl
Externí odkaz:
http://arxiv.org/abs/1808.09819
Autor:
Gulrajani, Ishaan, Kumar, Kundan, Ahmed, Faruk, Taiga, Adrien Ali, Visin, Francesco, Vazquez, David, Courville, Aaron
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very we
Externí odkaz:
http://arxiv.org/abs/1611.05013