Hands-on reinforcement learning algorithms with Python : learn, understand, and develop smart algorithms for addressing AI challenges

Hands-on reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges

"Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Key Features Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks Understand and develop model-free and model-based algorithms for buildin...

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Bibliographic Details
Other Authors: Lonza, Andrea, author (author)
Format: eBook
Language:Inglés
Published: Birmingham ; Mumbai : Packt Publishing 2019.
Edition:1st edition
Subjects:
Computer algorithms.
Python (Computer program language)
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630789506719
Table of Contents:
  • Chapter 1: The Landscape of Reinforcement Learning
  • Chapter 2: Implementing RL Cycle and OpenAI Gym
  • Chapter 3: Solving Problems with Dynamic Programming
  • Chapter 4: Q-Learning and SARSA Applications
  • Chapter 5: Deep Q-Network
  • Chapter 6: Learning Stochastic and PG Optimization
  • Chapter 7: TRPO and PPO Implementation
  • Chapter 8: DDPG and TD3 Applications
  • Chapter 9: Model-Based RL
  • Chapter 10: Imitation Learning with the DAgger Algorithm
  • Chapter 11: Understanding Black-Box Optimization Algorithms
  • Chapter 12: Developing the ESBAS Algorithm
  • Chapter 13: Practical Implementation for Resolving RL Challenges

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