Reinforcement Learning for Pacman

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Reinforcement Learning Basics

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

Deep Learning Agents: Reinforcement Learning

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

Acquiring Data for Learning: Modern Approaches and Challenges

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Vision-based robotic perception

Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.

Learning Agents: Exploration-Exploitation Tradeoff

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

Reactive Agents: Decision Processes

Explores reactive agents and decision processes, covering topics such as Markov Decision Processes and real-time agents.

Project-Based Learning in Communications and Robotics

Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Policy Gradient Methods: Convergence and Optimization

Covers the convergence of policy gradient methods and their optimization in reinforcement learning.

Deep Reinforcement Learning: Proximal Policy Optimization Techniques

Covers deep reinforcement learning techniques for continuous control, focusing on proximal policy optimization methods and their advantages over standard policy gradient approaches.