Deep Reinforcement Learning Frontiers of Artificial Intelligence /
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in...
| Main Author: | |
|---|---|
| Corporate Author: | |
| Language: | English |
| Published: |
Singapore :
Springer Singapore : Imprint: Springer,
2019.
|
| Edition: | 1st ed. 2019. |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-981-13-8285-7 |
Table of Contents:
- Introduction to Reinforcement Learning
- Mathematical and Algorithmic understanding of Reinforcement Learning
- Coding the Environment and MDP Solution
- Temporal Difference Learning, SARSA, and Q Learning
- Q Learning in Code
- Introduction to Deep Learning
- Implementation Resources
- Deep Q Network (DQN), Double DQN and Dueling DQN
- Double DQN in Code
- Policy-Based Reinforcement Learning Approaches
- Actor-Critic Models & the A3C
- A3C in Code
- Deterministic Policy Gradient and the DDPG
- DDPG in Code.