GrokkingDeepReinforcementLearning(2020)[Morales][9781617295454]

Description

foreword preface acknowledgments about this book about the author Introduction todeep reinforcement learning What is deep reinforcement learning? The past, present, and future of deep reinforcement learning The suitability of deep reinforcement learning Setting clear two-way expectations Mathematical foundationsof reinforcement learning Components of reinforcement learning MDPs: The engine of the environment Balancing immediateand long-term goals The objective of a decision-making agent Planning optimal sequences of actions Balancing the gatheringand use of information The challenge of interpreting evaluative feedback Strategic exploration Evaluatingagents’ behaviors Learning to estimate the value of policies Learning to estimate from multiple steps Improvingagents’ behaviors The anatomy of reinforcement learning agents Learning to improve policies of behavior Decoupling behavior from learning Achieving goals moreeffectively and efficiently Learning to improve policies using robust targets Agents that interact, learn, and plan Introduction to value-baseddeep reinforcement learning The kind of feedback deep reinforcement learning agents use Introduction to function approximation for reinforcement learning NFQ: The first attempt at value-based deep reinforcement learning More stablevalue-based methods DQN: Making reinforcement learning more like supervised learning Double DQN: Mitigating the overestimation of action-value functions Sample-efficientvalue-based methods Dueling DDQN: A reinforcement-learning-aware neural network architecture PER: Prioritizing the replay of meaningful experiences Policy-gradient andactor-critic methods REINFORCE: Outcome-based policy learning VPG: Learning a value function A3C: Parallel policy updates GAE: Robust advantage estimation A2C: Synchronous policy updates Advancedactor-critic methods DDPG: Approximating a deterministic policy TD3: State-of-the-art improvements over DDPG SAC: Maximizing the expected return and entropy PPO: Restricting optimization steps Toward artificialgeneral intelligence What was covered and what notably wasn’t? More advanced concepts toward AGI What happens next? index