Deep Reinforcement Learning with Python
portes grátis
Deep Reinforcement Learning with Python
RLHF for Chatbots and Large Language Models
Sanghi, Nimish
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
07/2024
634
Mole
9798868802720
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: The Foundation - Markov Decision Processes.- Chapter 3: Model Based Approaches.- Chapter 4: Model Free Approaches.- Chapter 5: Function Approximation and Deep Reinforcement Learning.- Chapter 6: Deep Q-Learning (DQN).- Chapter 7: Improvements to DQN.- Chapter 8: Policy Gradient Algorithms.- Chapter 9: Combining Policy Gradient and Q-Learning.- Chapter 10: Integrated Planning and Learning.- Chapter 11: Proximal Policy Optimization (PPO) and RLHF.- Chapter 12: Introduction to Multi Agent RL (MARL).- Chapter 13: Additional Topics and Recent Advances.
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Artificial Intelligence;Deep Reinforcement Learning;PyTorch;Neural Networks;Robotics;Autonomous Vehicle;Machine Learning;Markov Decision Processes;OpenAI Gym;Deep Q - Learning
Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: The Foundation - Markov Decision Processes.- Chapter 3: Model Based Approaches.- Chapter 4: Model Free Approaches.- Chapter 5: Function Approximation and Deep Reinforcement Learning.- Chapter 6: Deep Q-Learning (DQN).- Chapter 7: Improvements to DQN.- Chapter 8: Policy Gradient Algorithms.- Chapter 9: Combining Policy Gradient and Q-Learning.- Chapter 10: Integrated Planning and Learning.- Chapter 11: Proximal Policy Optimization (PPO) and RLHF.- Chapter 12: Introduction to Multi Agent RL (MARL).- Chapter 13: Additional Topics and Recent Advances.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
