Lecture 4, 2025, POMDP, Systems with Changing Parameters, Adaptive Control, Model Predictive Control

(1:50:32)

Lecture 3, 2025, LQ Problems, Approximation in Value Space, VI, and PI, Newton's Method, Examples

(1:25:24)

Computer chess with model predictive control and reinforcement learning

(36:17)

Lecture 2, 2025, Stochastic finite and infinite horizon DP, approximation in value and policy space

(2:6:50)

Lecture 1, 2025, course overview: RL and DP, AlphaZero, deterministic DP, examples, applications

(2:4:16)

Plenary lecture at IFAC NMPC, Kyoto, 24 Aug 2024

(54:31)

Lecture 1, 2024, course overview: RL and DP, AlphaZero, discrete and continuous applications

(1:59:20)

Lecture 13 2024: Approximate LP. Approximation in policy space, policy gradient methods. Epilogue

(1:21:8)

Lecture 12 2024; Off-line training with neural nets for approximate VI and PI. Aggregation

(1:29:29)

Lecture 11, 2024: On-line training, neural networks, and other approximation architectures

(1:38:4)

LIDS@80: Honoring Dimitri Bertsekas

(20:3)

Lecture 1, 2025, course overview: RL and DP, AlphaZero, deterministic DP, examples, applications

(2:4:16)

Dimitri P. Bertsekas - Optimization Society Prize

(16:42)

Dimitri Bertsekas, Convex Optimization: A Journey of 60 Years, Lecture at MIT

(24:30)

Lecture 1, 2024, course overview: RL and DP, AlphaZero, discrete and continuous applications

(1:59:20)

Plenary lecture at IFAC NMPC, Kyoto, 24 Aug 2024

(54:31)

Dimitri Bertsekas - Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control

(1:13:38)

Lecture 2, 2025, Stochastic finite and infinite horizon DP, approximation in value and policy space

(2:6:50)

Dimitri Bertsekas: Stable Optimal Control and Semicontractive Dynamic Programming

(1:7:5)

Approximate Dynamic Learning - Dimitri P. Bertsekas (Lecture 2, Part A)

(53:29)

Approximate Dynamic Learning - Dimitri P. Bertsekas (Lecture 2, Part B)

(34:14)

Lecture 13, 2021: An overview of the entire course. Discussion. ASU.

(2:37:1econd)

The Brussels Choice - Numberphile

(16:38)

Feature Based Aggregation and Deep Reinforcement Learning

(1:12:15)

Lecture 12, 2021: Aggregation methods and approximation in value space. ASU.

(2:11:8)