Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Tao, Ruo Yu"'
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
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definition and novel measure of interferen
Externí odkaz:
http://arxiv.org/abs/2307.04887
In many, if not every realistic sequential decision-making task, the decision-making agent is not able to model the full complexity of the world. The environment is often much larger and more complex than the agent, a setting also known as partial ob
Externí odkaz:
http://arxiv.org/abs/2211.07805
We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives. Our approach uses intrinsic rewards that are based on the distance
Externí odkaz:
http://arxiv.org/abs/2009.13579
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success. Recent attempts at solving text-based games with deep reinforcement learning have focused on the latter, i.e., lea
Externí odkaz:
http://arxiv.org/abs/1812.00855
Autor:
Côté, Marc-Alexandre, Kádár, Ákos, Yuan, Xingdi, Kybartas, Ben, Barnes, Tavian, Fine, Emery, Moore, James, Tao, Ruo Yu, Hausknecht, Matthew, Asri, Layla El, Adada, Mahmoud, Tay, Wendy, Trischler, Adam
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracki
Externí odkaz:
http://arxiv.org/abs/1806.11532
Autor:
C��t��, Marc-Alexandre, K��d��r, ��kos, Yuan, Xingdi, Kybartas, Ben, Barnes, Tavian, Fine, Emery, Moore, James, Tao, Ruo Yu, Hausknecht, Matthew, Asri, Layla El, Adada, Mahmoud, Tay, Wendy, Trischler, Adam
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracki
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4eacd08ce64b2f1f9bb99bfb23c05813
http://arxiv.org/abs/1806.11532
http://arxiv.org/abs/1806.11532