Deep Reinforcement Learning in Python

Deep Reinforcement Learning in Python
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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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