Reinforcement Learning Basics

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Continuous Reinforcement Learning: Advanced Machine Learning

Explores continuous-state reinforcement learning challenges, value function estimation, policy gradients, and Policy learning by Weighted Exploration.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Reinforcement Learning: Basics

Covers the basics of reinforcement learning, including Q-learning and neural networks.

Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Collective Learning Dynamics: Similarity Exploitation

Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained

Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.