Automated planning and scheduling

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Related lectures (11)

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.

Factored Representations: Planning Efficiency

Explores factored representations for planning, focusing on reducing complexity and improving efficiency through separate feature modeling.

Goal et objectifs

Emphasizes the importance of setting clear learning objectives and goals for effective online teaching.

Policy Iteration and Linear Programming in MDPs

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

Intelligent Agents: Making Decisions and Planning

Covers intelligent agents, decision-making, planning, machine learning, and game theory.

Markov Decision Processes: Dynamic Programming Techniques

Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.

Deliberative Agents: Planning and Strategies

Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Model-Based Deep RL: Planning and VAST

Covers model-based reinforcement learning, planning, variational state tabulation, and efficient Q- and V-values updating.

Monte Carlo Tree Search and Alpha Zero

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Multiagent Systems: Planning and Coordination

Explores the use of multi-agent systems for planning, coordination, and task assignment among heterogeneous agents.

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