Deep Reinforcement Learning made-easy

About Deep Reinforcement Learning made-easy

This course is the integration of deep learning and reinforcement learning. The course will introduce student with deep neural networks (DNN) starting from simple neural networks (NN) to recurrent neural network and long-term short-term memory networks. NN and DNN are the part of reinforcement learning (RL) agent so the students will be explained how to design custom RL environments and use them with RL agents. After the completion of the course the students will be able:

To understand deep learning and reinforcement learning paradigms

To understand Architectures and optimization methods for deep neural network training

To implement deep learning methods within Tensor Flow and apply them to data.

To understand the theoretical foundations and algorithms of reinforcement learning.

To apply reinforcement learning algorithms to environments with complex dynamics.

Course Contents:

Introduction to Deep Reinforcement Learning

Artificial Neural Network (ANN)

ANN to Deep Neural Network (DNN)

Deep Learning Hyperparameters: Regularization

Deep Learning Hyperparameters: Activation Functions and Optimizations

Convolutional Neural Network (CNN)

CNN Architecture

Recurrent Neural Network (RNN)

RNN for Long Sequences

LSTM Network

Overview of Markov Decision Processes

Bellman Equations and Value Functions

Deep Reinforcement Learning with Q-Learning

Model-Free Prediction

Deep Reinforcement Learning with Policy Gradients

Exploration and Exploitation in Reinforcement Learning