Cargando…

Reinforcement Learning

Detalles Bibliográficos
Autor principal:	Winder, Phil
Lenguaje:	eng
Publicado:	O'Reilly Media Inc 2020
Materias:	XX
Acceso en línea:	http://cds.cern.ch/record/2735803

Ejemplares similares

The Reinforcement Learning Workshop
por: Palmas, Alessandro
Publicado: (2020)

Statistical Reinforcement Learning
por: Sugiyama, Masashi
Publicado: (2015)

Deep Reinforcement Learning in Action
por: Brown, Brandon
Publicado: (2020)

Deep Reinforcement Learning Hands-On
por: Lapan, Maxim
Publicado: (2020)

Hands-On Reinforcement Learning for Games
por: Lanham, Micheal
Publicado: (2020)

Deep Reinforcement Learning with Python
por: Ravichandiran, Sudharsan
Publicado: (2020)

Mastering Reinforcement Learning with Python
por: Bilgin, Enes
Publicado: (2020)

Grokking Deep Reinforcement Learning
por: Morales, Miguel
Publicado: (2020)

An introduction to deep reinforcement learning
por: François-Lavet, Vincent, et al.
Publicado: (2018)

Reinforcement Learning Pocket Reference
por: Kirk, Matt
Publicado: (2021)

Bayesian reinforcement learning: a survey
por: Ghavamzadeh, Mohammed, et al.
Publicado: (2015)

TensorFlow 2 Reinforcement Learning Cookbook
por: Palanisamy, Praveen
Publicado: (2021)

Deep reinforcement learning: frontiers of artificial intelligence
por: Sewak, Mohit
Publicado: (2019)

Deep Reinforcement Learning in Unity: With Unity ML Toolkit
por: Majumder, Abhilash
Publicado: (2020)

PyTorch 1.x Reinforcement Learning Cookbook
por: Liu, Yuxi
Publicado: (2019)

Foundations of Deep Reinforcement Learning: Theory and Practice in Python
por: Graesser, Laura
Publicado: (2019)

A tutorial on linear function approximators for dynamic programming and reinforcement learning
por: Geramifard, Alborz, et al.
Publicado: (2013)

R machine learning projects: implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
por: Chinnamgari, Sunil Kumar
Publicado: (2019)

Training Reinforcement
por: Wurth, Anthonie
Publicado: (2018)

Towards user-centric intelligent network selection in 5G heterogeneous wireless networks: a reinforcement learning perspective
por: Du, Zhiyong, et al.
Publicado: (2019)

Geosynthetic Reinforced Soil (GRS) Walls
por: Wu, Jonathan
Publicado: (2019)

Nanoclay reinforced polymer composites: nanocomposites and bionanocomposites
por: Jawaid , Mohammad, et al.
Publicado: (2016)

Reinforced concrete fundamentals /
por: Ferguson, Phil Moss, 1899-
Publicado: (1988)

Nanoclay reinforced polymer composites: natural fibrenanoclay hybrid composites
por: Jawaid, Mohammad, et al.
Publicado: (2016)

Reinforcement learning : an introduction /
por: Sutton, Richard S.
Publicado: (1998)

Reinforcement learning: an introduction
por: Sutton, Richard S, et al.
Publicado: (2018)

Algorithms for Reinforcement Learning
por: Szepesvari, Csaba
Publicado: (2010)

Bringing Out the Best in People: How to Apply the Astonishing Power of Positive Reinforcement
por: Daniels, Aubrey
Publicado: (2016)

Hands-on reinforcement learning with Python: master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
por: Ravichandiran, Sudharsan
Publicado: (2018)

Reinforcement Learning: State-of-the-Art
por: Wiering, Marco, et al.
Publicado: (2012)

Reinforcement Learning or Active Inference?
por: Friston, Karl J., et al.
Publicado: (2009)

Reinforcement learning in dendritic structures
por: Schiess, Mathieu, et al.
Publicado: (2011)

Evolution with Reinforcement Learning in Negotiation
por: Zou, Yi, et al.
Publicado: (2014)

Benchmarking for Bayesian Reinforcement Learning
por: Castronovo, Michael, et al.
Publicado: (2016)

Reinforcement Learning for Precision Oncology
por: Eckardt, Jan-Niklas, et al.
Publicado: (2021)

Learn to Steer through Deep Reinforcement Learning
por: Wu, Keyu, et al.
Publicado: (2018)

Learning Macromanagement in Starcraft by Deep Reinforcement Learning
por: Huang, Wenzhen, et al.
Publicado: (2021)

Keras reinforcement learning projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
por: Ciaburro, Giuseppe
Publicado: (2018)

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning
por: Park, Jong-Rul, et al.
Publicado: (2020)

Statistical reinforcement learning: modern machine learning approaches
por: Sugiyama, Masashi
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_v7m35kouf7n29jl07jsqjgol7q