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 paradigmsTo understand Architectures and optimization methods for deep neural network trainingTo 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 LearningArtificial Neural Network (ANN)ANN to Deep Neural Network (DNN)Deep Learning Hyperparameters: RegularizationDeep Learning Hyperparameters: Activation Functions and OptimizationsConvolutional Neural Network (CNN)CNN ArchitectureRecurrent Neural Network (RNN)RNN for Long SequencesLSTM NetworkOverview of Markov Decision ProcessesBellman Equations and Value FunctionsDeep Reinforcement Learning with Q-LearningModel-Free PredictionDeep Reinforcement Learning with Policy GradientsExploration and Exploitation in Reinforcement Learning