Reinforcement Learning: Basics and Applications

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Deep Reinforcement Learning: Mini-Batches and Policy Methods

Discusses deep reinforcement learning methods, focusing on mini-batches and the implications of on-policy and off-policy training techniques.

Learning-aided Program Reasoning

Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Landscape and Generalisation in Deep Learning

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

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

MuZero: Planning and Learning Model

Covers MuZero, a model that learns to predict rewards and actions iteratively, achieving state-of-the-art performance in board games and Atari video games.

Monte Carlo Tree Search and Alpha Zero

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

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

Reinforcement Learning: BackUp Diagrams

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