Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice

Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

• Understand each key aspect of a deep RL problem
• Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
• Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
• Understand how algorithms can be parallelized synchronously and asynchronously
• Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
• Explore algorithm benchmark results with tuned hyperparameters
• Understand how deep RL environments are designed