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Deep Reinforcement Learning in Python

Deep Reinforcement Learning in Python - Laura Harding Graesser

Deep Reinforcement Learning in Python

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
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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
Citeste mai mult

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