Lectures related to TD Learning: Temporal Difference Learning

Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.

Reinforcement Learning: TD Learning and SARSA Variants

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

Model-Free Prediction in Reinforcement Learning: Key Methods

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

Introduction to Reinforcement Learning: Concepts and Applications

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

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.

Interactive Lecture: Reinforcement Learning

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

Scalable Multi-query Execution: Reinforcement Learning Approach

Presents a novel approach to scalable multi-query execution using reinforcement learning.

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.

Deep Learning Agents: Reinforcement Learning

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

Reinforcement Learning: SARSA Algorithm

Explores the SARSA algorithm for reinforcement learning, focusing on updating Q-values and the importance of exploration in learning by rewards.

Continuous Reinforcement Learning: Advanced Machine Learning

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

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

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.

Relation of SARSA and Bellman equation

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

Risk Minimization from Adaptively Collected Data

Explores risk minimization from adaptively collected data with guarantees for policy learning and the importance of exploration strategies.

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Policy Iteration and Linear Programming in MDPs

Discusses policy iteration and linear programming methods for solving Markov Decision Processes.

Evolution of Migration Policy: Analysis and Evaluation

Covers assignments for tracing migration policy evolution and evaluating its impact.

Policy Gradient Methods: Single Neuron Example

Covers policy gradient methods using a single neuron with binary output.

Dynamic Programming: Optimal Control

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.