Reinforcement Learning

Learn reinforcement learning from scratch.

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices

Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes

Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries

Dive into advanced deep reinforcement learning algorithms using PyTorch 1.x

Solve real-world problems by employing reinforcement learning techniques with Java

Leverage TRFL to create and implement RL models with ease

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks

Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries

A practical guide to mastering reinforcement learning algorithms using Keras

Train your agent using Reinforcement Learning with Tensorflow’s neural networks, OpenAI Gym and Python, to make it smarter

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python