Lectures in course CS-456: Deep reinforcement learning

General Introduction to Artificial Neural Networks

Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Exploration versus Exploitation

Explores the balance between exploring new possibilities and exploiting known rewarding actions in reinforcement learning.

Reinforcement Learning: SARSA Algorithm

Explores the SARSA algorithm for reinforcement learning, focusing on updating Q-values and the importance of exploration in learning by rewards.

Markov Decision Processes: Dynamic Programming Techniques

Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.

General Introduction to Artificial Neural Networks: Image classification

Introduces artificial neural networks for image classification using layered feedforward and recurrent networks.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Policy Iteration and Linear Programming in MDPs

Discusses policy iteration and linear programming methods for solving Markov Decision Processes.

Introduction to Supervised Learning: Classification and Perceptrons

Explores supervised learning through classification as a geometric problem and the concept of finding a separating surface.

Introduction to Supervised Learning

Introduces supervised learning using labeled data points to optimize classifier output.

Introduction: classification by a simple perceptron

Covers the concept of a simple perceptron and its ability to solve linearly separable problems by imposing a separating hyperplane.

Introduction to the Perceptron Algorithm

Introduces the perceptron algorithm and its geometric interpretation, emphasizing the rotation of the hyperplane due to misclassified patterns.

Introduction to Learning by Stochastic Gradient Descent: Simple Perceptron

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Gradient Descent Methods for Artificial Neural Networks

Explores gradient descent methods for training artificial neural networks, covering supervised learning, single-layer networks, and modern gradient descent rules.

Deep Reinforcement Learning: Policy Gradient and Value Function

Explains the implementation of the REINFORCE algorithm with a baseline using a neural network.

XOR Problem: Neural Networks

Delves into solving the XOR problem using a two-layer neural network.

Actor-Critic Architecture and Advantage-Actor-Critic

Explores actor-critic networks and the advantage actor critic method for optimizing parameters to maximize return.

Multilayer Networks: First Steps

Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.