Lectures related to Intelligent Agents: Making Decisions and Planning

Socially-aware Artificial Intelligence for Last-mile Mobility

Explores socially-aware AI for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements.

Autonomous Vehicles: Intelligence and Perception

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

Zero-Sum Games: Concepts and Applications

Covers zero-sum games, focusing on Nash equilibria, security strategies, and their applications in various scenarios.

Safe Learning for Autonomous Systems

Explores challenges in control, safety, and coordination for autonomous systems like autonomous cars, focusing on safe learning and Nash equilibria.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

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.

Multi-Agent Systems: Traffic Management and Pricing Strategies

Covers multi-agent systems in traffic management and dynamic pricing strategies using game theory.

Game Theory: Stackelberg and Backward Induction

Discusses Stackelberg games and backward induction, illustrating game theory concepts through examples and applications in real-world scenarios.

Learning Agents: Exploration-Exploitation Tradeoff

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

Static Games: Introduction to Game Theory Concepts

Covers the basics of static games and introduces key game theory concepts, including the Prisoner's Dilemma and Nash Equilibrium.

Markov Games: Concepts and Applications in Reinforcement Learning

Covers Markov games, their dynamics, equilibria, and applications in reinforcement learning.

Autonomous Vehicles: Trajectory Prediction and Social Behavior

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Introduction to Game Theory

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

AI for Science

Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.

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.

Model-based versus Model-free Reinforcement Learning

Compares model-based and model-free reinforcement learning, highlighting the advantages of the former in adapting to reward changes and planning future actions.

Reinforcement Learning: Q-Learning

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

Deep Learning Agents: Reinforcement Learning

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

Reinforcement Learning Basics

Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.