In just a few years deep reinforcement learning (DRL) systems such as DeepMinds DQN have
yielded remarkable results. This hybrid approach to machine learning shares many similarities
with human learning: its unsupervised self-learning self-discovery of strategies usage of
memory balance of exploration and exploitation and its exceptional flexibility. Exciting in
its own right DRL may presage even more remarkable advances in general artificial
intelligence. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and
most accessible way to get started with DRL. The authors teach through practical hands-on
examples presented with their advanced OpenAI Lab framework. While providing a solid
theoretical overview they emphasize building intuition for the theory rather than a deep
mathematical treatment of results. Coverage includes: Components of an RL system including
environment and agents Value-based algorithms: SARSA Q-learning and extensions offline
learning Policy-based algorithms: REINFORCE and extensions comparisons with value-based
techniques Combined methods: Actor-Critic and extensions scalability through async methods
Agent evaluation Advanced and experimental techniques and more How to achieve breakthrough
machine learning performance by combining deep neural networks with reinforcement learning
Reduces the learning curve by relying on the authors' OpenAI Lab framework: requires less
upfront theory math and programming expertise Provides well-designed modularized and tested
code examples with complete experimental data sets to illuminate the underlying algorithms
Includes case studies practical tips definitions and other aids to learning and mastery
Prepares readers for exciting future advances in artificial general intelligence The accessible
hands-on full-color tutorial for building practical deep reinforcement learning solutions How
to achieve breakthrough machine learning performance by combining deep neural networks with
reinforcement learning Reduces the learning curve by relying on the authors' OpenAI Lab
framework: requires less upfront theory math and programming expertise Provides well-designed
modularized and tested code examples with complete experimental data sets to illuminate the
underlying algorithms Includes case studies practical tips definitions and other aids to
learning and mastery Prepares readers for exciting future advances in artificial general
intelligence
