Lectures related to Reinforcement Learning: Q-Learning

Reinforcement Learning: TD Learning and SARSA Variants

Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.

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.

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.

Understanding Neurorobotics

Explores the link between intelligence, the brain, and behavior through neurorobotics.

Environmental Image Analysis

Explores Valérie's project on automating land area statistics using deep learning algorithms and aerial images.

Motor Control in Vertebrates

Explores the hierarchical organization of motor control in vertebrates and layered control architectures.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Advanced Machine Learning: Discrete Reinforcement Learning

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

TD Learning: Temporal Difference Learning

Covers Temporal Difference Learning, V-values, state-values, and TD methods in reinforcement learning.

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.

Controlling Behavior in Animals and Robots

Explores embodied behavioral control in animals and robots through group presentations and hands-on exercises.

Reinforcement Learning: Basics and Applications Log in to Mediaspace to watch this video

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

Interactive Lecture: Reinforcement Learning Log in to Mediaspace to watch this video

Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Introduction to Reinforcement Learning: Concepts and Applications Log in to Mediaspace to watch this video

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

Model-Free Prediction in Reinforcement Learning: Key Methods Log in to Mediaspace to watch this video

Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.

Deep Learning Agents: Reinforcement Learning Log in to Mediaspace to watch this video

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

Neuroscience and Machine Learning: Understanding Visual Intelligence Log in to Mediaspace to watch this video

Explores the relationship between neuroscience and machine learning in visual intelligence.

Reinforcement Learning Concepts Log in to Mediaspace to watch this video

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Learning Agents: Exploration-Exploitation Tradeoff Log in to Mediaspace to watch this video

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

Generative AI and Reinforcement Learning: Future Directions Log in to Mediaspace to watch this video

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.