bellman

The 20 most recent episodes and tracks on this topic.

Saves to your Watch queue, to pick up on another day or another device.

Pick anything below and it plays in the bar at the foot of the window — and keeps playing while you go on browsing the directory.

Session 21: Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum LearningJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 20: Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCEJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 19: Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax RegressionJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., ConvergenceJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 17: Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0Jadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 16 γ contraction, Banach's Fixed Point Theorem, How far is it far from the intended optimalJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 15 TD(0) convergence proof (contd), Point of Convergence of TD(0) (linear function approx.)Jadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 14: TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)Jadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 13: Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) ImplementationJadavpur University, 2025: Introduction to Reinforcement Learning Notes
Session 12: On Policy vs Off Policy Algorithms, Importance Sampling, Model-free Q learning, SARSAJadavpur University, 2025: Introduction to Reinforcement Learning Notes
L4: Value Iteration and Policy Iteration (P3-Truncated policy iteration)—Math Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L4: Value Iteration and Policy Iteration (P2-Policy iteration)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L4: Value Iteration and Policy Iteration (P1-Value iteration)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L3: Bellman Optimality Equation (P4-Interesting properties)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L3: Bellman Optimality Equation (P3-More)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L3: Bellman Optimality Equation (P2-Optimal policy)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L3: Bellman Optimality Equation (P1-Motivating example)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L2: Bellman Equation (P4-Matrix-vector form and solution)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L2: Bellman Equation (P5-Action value)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes
L2: Bellman Equation (P3-Bellman equation-Derivation)—Mathematical Foundations of RLMathematical Foundations of Reinforcement Learning Notes

This playlist:.m3u .pls All the feeds behind it