Lectures related to Relation of SARSA and Bellman equation

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.

Interactive Lecture: Reinforcement Learning

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

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.

Introduction to Reinforcement Learning: Concepts and Applications

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

Reinforcement Learning: Q-Learning

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

Gradient Descent

Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.

Evolution of Migration Policy: Analysis and Evaluation

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

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.

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.

Research Paper Assignment: Policy Evolution in Migration Governance

Explains the second-semester research paper assignment on analyzing policy evolution in migration governance.

Multi-arm Bandits: Exploration vs Exploitation

Explores the balance between exploration and exploitation in multi-arm bandit algorithms.

Policy, People, Technology: Future of Mobility

Explores the future of mobility through policy, people, and technology.

Innovative Governance of Large Urban Systems

Delves into the governance of large urban systems and the future of mobility, addressing challenges and new business models.

Towards Sustainable Composites

Explores sustainable composite solutions, including recycling, end-of-life strategies, and hydrogen storage.

Scalable Multi-query Execution: Reinforcement Learning Approach

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

Dynamic Programming: Optimal Control

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

Deep Learning for Autonomous Vehicles

Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Deep Learning Agents: Reinforcement Learning

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