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CS-456: Deep reinforcement learning

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Lectures in this course (96)

Learning to Find a Goal

Delves into a biologically inspired version of Reinforcement Learning, focusing on maze navigation and the implementation of spiking neurons.

Regularization Methods: Training and Validation Base

Explores regularization methods in neural networks, emphasizing the importance of training and validation bases to prevent overfitting.

Model-based versus Model-free Reinforcement Learning

Compares model-based and model-free reinforcement learning, highlighting the advantages of the former in adapting to reward changes and planning future actions.

Careful Cross-validation

Emphasizes the significance of careful cross-validation in deep neural networks, including the split of data and the concept of K-fold cross-validation.

Regularization by Early Stopping

Explores regularization by early stopping in deep neural networks to control flexibility and avoid overfitting.

Reinforcement Learning: Reward-based Learning

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Elements of Reinforcement Learning

Introduces the fundamental elements of Reinforcement Learning and demonstrates their application with the Acrobot system.

Elements of Reinforcement Learning: Quiz on States

Features a quiz on the number of discrete states in backgammon, highlighting the immense complexity of reinforcement learning applications.

Reinforcement Learning: One-step Horizon (Bandit Problems)

Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.

Introduction to Supervised Learning: Classification and Perceptrons

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

Reinforcement Learning: Bandit Problems

Covers the convergence in expectation for the Q value in reinforcement learning.

Exploration vs. Exploitation: Softmax Policy Quiz

Presents a quiz on the exploration vs. exploitation dilemma using the softmax policy.

Bellman Equation: Value Consistency and Optimal Actions

Covers the Bellman equation, Q-values, discount factor, and optimal actions.

Relation of SARSA and Bellman equation

Explores the relation between fluctuating Q-values in SARSA and the Bellman equation through expectations and policy constancy.

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

Tricks of the Trade in Deep Learning: Aims

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

Proximal Policy Optimization for Continuous Control

Explores Proximal Policy Optimization for enhancing stability and efficiency in continuous control with deep reinforcement learning.

Deep Deterministic Policy Gradient for Continuous Control

Presents the Deep Deterministic Policy Gradient algorithm for training neural networks in continuous action spaces efficiently.

Introduction to Supervised Learning

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

Theory of Bagging

Explores the theory of bagging, demonstrating how it improves model performance and the importance of uncorrelated data for its success.

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