Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Lobel, Sam"'
Autor:
Allen, Cameron, Kirtland, Aaron, Tao, Ruo Yu, Lobel, Sam, Scott, Daniel, Petrocelli, Nicholas, Gottesman, Omer, Parr, Ronald, Littman, Michael L., Konidaris, George
Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can a
Externí odkaz:
http://arxiv.org/abs/2407.07333
Autor:
Lobel, Sam, Parr, Ronald
We present a bound for value-prediction error with respect to model misspecification that is tight, including constant factors. This is a direct improvement of the "simulation lemma," a foundational result in reinforcement learning. We demonstrate th
Externí odkaz:
http://arxiv.org/abs/2406.16249
We propose a new method for count-based exploration in high-dimensional state spaces. Unlike previous work which relies on density models, we show that counts can be derived by averaging samples from the Rademacher distribution (or coin flips). This
Externí odkaz:
http://arxiv.org/abs/2306.03186
Autor:
Gottesman, Omer, Asadi, Kavosh, Allen, Cameron, Lobel, Sam, Konidaris, George, Littman, Michael
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show that, unfortunately, this property fails to hold in many typical doma
Externí odkaz:
http://arxiv.org/abs/2110.12276
Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural networks into latent variable
Externí odkaz:
http://arxiv.org/abs/1906.04281