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Machine learning. --- Reinforcement learning. --- Artificial intelligence. --- Apprentissage automatique. --- Apprentissage par renforcement (intelligence artificielle) --- Intelligence artificielle. --- TensorFlow (Electronic resource)

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"In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: Components of an RL system, including environment and agents ; Value-based algorithms: SARSA, Q-learning and extensions, offline learning ; Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques ; Combined methods: Actor-Critic and extensions; scalability through async methods ; Agent evaluation ; Advanced and experimental techniques, and more" [Publisher]

Artificial intelligence. --- Intelligence artificielle. --- Machine learning. --- Apprentissage automatique. --- Reinforcement learning. --- Apprentissage par renforcement (intelligence artificielle) --- Python (Computer program language) --- Python (langage de programmation) --- Apprentissage automatique

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This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.

Machine learning. --- Genetic algorithms. --- Reinforcement learning. --- Apprentissage automatique --- Algorithmes génétiques --- Apprentissage par renforcement (Intelligence artificielle) --- Engineering. --- Artificial intelligence. --- Bioinformatics. --- Mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Applications of Mathematics. --- Machine learning --- Genetic algorithms --- Reinforcement learning --- Applied Mathematics --- Computer Science --- Civil Engineering --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Learning, Machine --- Engineering --- Engineering analysis --- Math --- Bio-informatics --- Biological informatics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Construction --- Mathematics --- Applied mathematics. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Mathematical analysis --- Biology --- Information science --- Computational biology --- Systems biology --- Science --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Data processing