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Related lectures (32)
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Markov Decision Processes: Foundations of Reinforcement Learning
Covers Markov Decision Processes, their structure, and their role in reinforcement learning.
Nonlinear Model Predictive Control
Explores Nonlinear Model Predictive Control, covering stability, optimality, pitfalls, and examples.
Asset Pricing and Hedging in Complete Markets
Covers asset pricing, hedging, American claims, stopping times, and dynamic programming in finance.
Linear Request Problem: Rigorous Results
Delves into the linear request problem, infinitesimal perturbations, transport plans, and the main theorem from 2018.
The Transhipment Problem: Optimality Conditions
Covers the optimality conditions for solving transhipment problems efficiently.
Reinforcement Learning: Q-Learning
Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.
Policy Iteration and Linear Programming in MDPs
Discusses policy iteration and linear programming methods for solving Markov Decision Processes.
Stochastic Endowment Consumption Model
Covers the stochastic endowment consumption model, log-linear transformation, impulse response solution, and Matlab codes.
Dynamic Programming: Portfolio Optimization
Explores dynamic programming for optimizing portfolio choices and asset pricing theory.
Dynamic Portfolio Choice: Wealth Dynamics and HJB Equation
Covers dynamic portfolio choice, wealth dynamics, HJB equation, and asset pricing puzzles.
Reinforcement Learning: One-step Horizon (Bandit Problems)
Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.
Optimal Transport: Theory and Applications
Covers the theory and applications of optimal transport, focusing on infimal convolution and Kantorovich potentials.
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