Category
Reinforcement learning
Related lectures (141)
Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search
Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.
Introduction to Game Theory
Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.
Deliberative Agents: Planning and Strategies
Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.
Intelligent Agents: Making Decisions and Planning
Covers intelligent agents, decision-making, planning, machine learning, and game theory.
Agent Platforms: Types, Intelligence, and Communication
Explores agent platforms, intelligence, and communication models in multiagent systems.
Multiagent Systems: Planning and Coordination
Explores the use of multi-agent systems for planning, coordination, and task assignment among heterogeneous agents.
Reinforcement Learning Basics
Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.
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.
Reinforcement Learning: Q-Learning
Covers Q-Learning, a model-free reinforcement learning algorithm, and its application to Tic-Tac-Toe with examples and quizzes.
Safe Learning and Control
Explores safe learning, control, multi-agent coordination, and Nash equilibrium convergence in intelligent systems.