Reinforcement Learning
portes grátis
Reinforcement Learning
Theory and Python Implementation
Xiao, Zhiqing
Springer Verlag, Singapore
09/2024
559
Dura
Inglês
9789811949326
15 a 20 dias
Descrição não disponível.
Chapter 1. Introduction of Reinforcement Learning (RL).- Chapter 2. MDP: Markov Decision Process.- Chapter 3. Model-based Numerical Iteration.- Chapter 4. MC: Monte Carlo Learning.- Chapter 5. TD: Temporal Difference Learning.- Chapter 6. Function Approximation.- Chapter 7. PG: Policy Gradient.- Chapter 8. AC: Actor-Critic.- Chapter 9. DPG: Deterministic Policy Gradient.- Chapter 10. Maximum-Entropy RL.- Chapter 11. Policy-based Gradient-Free Algorithms.- Chapter 12. Distributional RL.- Chapter 13. Minimize Regret.- Chapter 14. Tree Search.- Chapter 15. More Agent-Environment Interfaces.- Chapter 16. Learn from Feedback and Imitation Learning.
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Reinforcement Learning;Deep Reinforcement Learning;Machine Learning;Artificial Intelligence;Python Implementations
Chapter 1. Introduction of Reinforcement Learning (RL).- Chapter 2. MDP: Markov Decision Process.- Chapter 3. Model-based Numerical Iteration.- Chapter 4. MC: Monte Carlo Learning.- Chapter 5. TD: Temporal Difference Learning.- Chapter 6. Function Approximation.- Chapter 7. PG: Policy Gradient.- Chapter 8. AC: Actor-Critic.- Chapter 9. DPG: Deterministic Policy Gradient.- Chapter 10. Maximum-Entropy RL.- Chapter 11. Policy-based Gradient-Free Algorithms.- Chapter 12. Distributional RL.- Chapter 13. Minimize Regret.- Chapter 14. Tree Search.- Chapter 15. More Agent-Environment Interfaces.- Chapter 16. Learn from Feedback and Imitation Learning.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
