Deep Reinforcement Learning with Python With PyTorch, TensorFlow and OpenAI Gym

You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods.
You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym.
What You'll LearnExamine deep reinforcement learningImplement deep learning algorithms using OpenAI's Gym environmentCode your own game playing agents for Atari using actor-critic algorithmsApply best practices for model building and algorithm trainingWho This Book Is For
Machine learning developers and architects who want to stay ahead of the curve in the field of AI and deep learning.


Deep Reinforcement Learning with Python With PyTorch, TensorFlow and OpenAI Gym Fast Download
Deep Reinforcement Learning with Python With PyTorch, TensorFlow and OpenAI Gym Full Download