Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Graesser, Laura; Keng, Wah Loon
Language: English
Published by Addison-Wesley Professional, 2019
- Softcover
- Used
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- Title
- Foundations of Deep Reinforcement Learning : Theory and Practice in Python
- Author
- Graesser, Laura; Keng, Wah Loon
- Publisher
- Addison-Wesley Professional
- Publication year
- 2019
- Condition
- As New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0135172381
- ISBN 13
- 9780135172384
- Series
- Book 8 of 14: Addison-Wesley Data & Analytics
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
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