TY  - BOOK
AU  - Lapan, Maxin
TI  - Deep reinforcement learning hands - on: Apply moder RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more 
SN  - 978-1-83882-6999-4 (Pasta rústica)
U1  - 006.31 L299d 2020 
PY  - 2020///
CY  - Reino Unido
PB  - Packt Publishing
KW  - APRENDIZAJE PROFUNDO ( APRENDIZAJE AUTOMÁTICO)
KW  - APRENDIZAJE AUTOMÁTICO (INTELIGENCIA ARTIFICIAL)
KW  - INGENIERIA DE SOFTWARE
KW  - PROCEDIMIENTO DE LENGUAJE NATURAL ( COMPUTADORES)
N1  - Incluye indice; Perface -- Chapter 1 : What is reinforcement learning ? -- Chapter 2 : OpenAL Gym -- Chapter 3: Deep learning with pytorch -- Chapter 4: The cross-Entropy Method -- Chapter 5 : Tabular learning and the bellman equation -- Chapter 6: Deep Q- Networks -- Chapter 7: Higher- level RL libraries -- Chapters 8: DQN extensions -- Chapter 9: Ways to speed up RL -- Chapter 10: Stock traing using RL -- Chapter 11: Policy grandients -an alternative -- Chapter 12: The actor - Critic Method -- Chapter 13: Asynchronous advantage actor - critic -- Chapter 14: Training chatbots with RL -- Chapter 15: : The texworld enviroment -- Chapter 16: Web navigation -- Chapter 17: Continuous action space -- Chapter 18: RL in robotics -- Chapter 19: Trus regions - PPO, TRPO, ACKT, and SAC -- Chapter 20: Black - box optimization in RL -- Chapter 21 Advanced exploration -- Chapter 22: Beyond model - free - imagination -- Chapter 23: AlphaGo zero -- Chapter 24: RL indiscrete optimization -- Chapter 25: Multi- agent RL
ER  -