Hands-on Q-learning with Python practical Q-learning with OpenAI Gym, Keras, and TensorFlow
"Leverage the power of reward-based training for your deep learning models with Python Key Features Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) Study practical deep reinforcement learning using Q-Networks Explore state-based unsupervised learnin...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai :
Packt
[2019]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630490006719 |
Tabla de Contenidos:
- Section 1. Q-Learning: A Roadmap. Brushing Up on Reinforcement Learning Concepts ; Getting Started with the Q-Learning Algorithm ; Setting Up Your First Environment with OpenAI Gym ; Teaching a Smartcab to Drive Using Q-Learning
- Section 2. Building and Optimizing Q-Learning Agents. Building Q-Networks with TensorFlow ; Digging Deeper into Deep Q-Networks with Keras and TensorFlow
- Section 3. Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym. Decoupling Exploration and Exploitation in Multi-Armed Bandits ; Further Q-Learning Research and Future Projects ; Assessments.
