Foundations of Deep Reinforcement Learning: Theory and Practice in Python
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. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then care…
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- Full title :Foundations of Deep Reinforcement Learning : Theory and Practice in Python.
Summary
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. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software.
Read More ↓Edition Statements
- Publication Date:2019.
Publication Statements
- Publisher(s):Pearson Education Limited
- Place of Publication:Sydney
- Language:eng
- Publication Date:2019.
Notes
Description based upon print version of record.
The Physical Object
- Description:1 online resource (515 p.).
Edition Identifiers
- ISBN:9780135172483, 0135172489
- oclc:on1338835718
- Dewey Decimal:006.31
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