In this 11-video course, learners can examine the role of reward and discount factors in reinforcement learning, as well as the multi-armed bandit problem and approaches to solving it for machine learning. You will begin by learning how to install the Markov Decision Policy (MDP) toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. Next, examine the role of reward and discount factors in reinforcement learning, and the multi-armed bandit problem and solutions. Learn about dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation. Then learners will explore reinforcement learning agent components and applications; work with reinforcement learning agents using Keras and OpenAI Gym; describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI; and implement deep Q-learning with Keras. Finally, observe how to train deep neural networks (DNN) with reinforcement learning for time series forecasting. In the closing exercise, you will recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning by using Keras and OpenAI Gym.

install the Markov Decision Policy toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm

recognize the role of reward and discount factors in reinforcement learning

describe the multi-armed bandit problem and different approaches of solving this problem

describe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation

list reinforcement learning agent components and reinforcement agent applications

work with reinforcement learning agents using Keras and OpenAI Gym

describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI

implement deep reinforcement learning or deep Q-learning using Keras and OpenAI Gym

recognize how to train deep neural networks using reinforcement learning for time series forecasting

recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning using Keras and OpenAI Gym