Continuous space: action space

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Theory of reinforcement learning: Introductory question

Covers the theory of reinforcement learning, exploring exploration/exploitation dilemma and continuous state/action spaces.

Eligibility Traces for Policy Gradient and Actor-Critic

Explores eligibility traces in policy gradient and actor-critic architectures, leading to an elegant online learning rule.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Modeling the input space

Explores modeling continuous input spaces in reinforcement learning using neural networks and radial basis functions.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Landscape and Generalisation in Deep Learning

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

Mathematics of Data: Optimization Basics

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Reinforcement Learning: Q-Learning

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Learning to Find a Goal

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

Data-driven Reinforcement Learning

Discusses challenges in AI systems, supervised learning limitations, and the necessity of data-driven methods in reinforcement learning.