Reinforcement Learning
portes grátis
Reinforcement Learning
Theory and Python Implementation
Xiao, Zhiqing
Springer Verlag, Singapore
07/2024
593
Dura
Inglês
9789811949326
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
1 Introduction of Reinforcement Learning (RL).- 2 MDP: Markov Decision Process.- 3 Model-based Numerical Iteration.- 4 MC: Monte Carlo Learning.- 5 TD: Temporal Difference Learning.- 6 Function Approximation.- 7 PG: Policy Gradient.- 8 AC: Actor-Critic.- 9 DPG: Deterministic Policy Gradient.- 10 Maximum-Entropy RL.- 11 Policy-Based Gradient-Free Algorithms.- 12 Distributional RL .- 13 Minimize Regret.- 14 Tree Search.- 15 IL: Imitation Learning.- 16 More Agent-Environment Interfaces.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Reinforcement Learning;Deep Reinforcement Learning;Machine Learning;Artificial Intelligence;Python Implementations
1 Introduction of Reinforcement Learning (RL).- 2 MDP: Markov Decision Process.- 3 Model-based Numerical Iteration.- 4 MC: Monte Carlo Learning.- 5 TD: Temporal Difference Learning.- 6 Function Approximation.- 7 PG: Policy Gradient.- 8 AC: Actor-Critic.- 9 DPG: Deterministic Policy Gradient.- 10 Maximum-Entropy RL.- 11 Policy-Based Gradient-Free Algorithms.- 12 Distributional RL .- 13 Minimize Regret.- 14 Tree Search.- 15 IL: Imitation Learning.- 16 More Agent-Environment Interfaces.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
