Lectures related to Infinite-Horizon Problems: Formulation & Complexity

Explores the Asset Selling Problem to maximize long-term reward without a deadline.

Covers decision-making in marketing based on customer behavior for optimal strategies.

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

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

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

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

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Covers the basics of discrete mathematics, focusing on logic, structures, and algorithms for computer systems.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

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

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

Explores the Simon Problem 4, analyzing quantum states and algorithm complexity.

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.