This improvement to DQN was proposed in 2015, in the paper called Dueling Network Architectures for Deep Reinforcement Learning ([8] Wang et al., 2015). The core observation of this paper lies in the fact that the Q-values Q(s, a) our network is trying to approximate can be divided into quantities: the value of the state V(s) and the advantage of actions in this state A(s, a). We've seen quantity V(s) before, as it was the core of the value iteration method from Chapter 5, Tabular Learning and the Bellman Equation. It just equals to the discounted expected reward achievable from this state. The advantage A(s, a) is supposed to bridge the gap from A(s) to Q(s, a), as, by definition: Q(s, a) = V(s) + A(s, a). In other words, the advantage A(s, a) is just the delta, saying how much extra reward some particular action from the state brings us. Advantage could be positive or negative and, in general, can have any magnitude. For example, at some tipping point, the choice of one action...
Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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Overview of this book
Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.
The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
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Current Title:
Deep Reinforcement Learning Hands-On
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Free Chapter
What is Reinforcement Learning?
OpenAI Gym
Deep Learning with PyTorch
The Cross-Entropy Method
Tabular Learning and the Bellman Equation
Deep Q-Networks
DQN Extensions
Stocks Trading Using RL
Policy Gradients – An Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Chatbots Training with RL
Web Navigation
Continuous Action Space
Trust Regions – TRPO, PPO, and ACKTR
Black-Box Optimization in RL
Beyond Model-Free – Imagination
AlphaGo Zero
Index
Customer Reviews