1:03:43Reinforcement Learning, Model Predictive Control, and the Newton Step for Solving Bellman's EquationDimitri Bertsekas8.5K views·11 months ago
1:25:33Lecture 12, 2025; Training of cost functions, approximation in policy space, policy gradient methodsDimitri Bertsekas1.4K views·1 year ago
1:08:20Abstract Dynamic Programming, Reinforcement Learning, Newton's Method, and Gradient OptimizationDimitri Bertsekas3.2K views·1 year ago
1:15:27Lecture 11, 2025; Adversarial Problems, Minimax Rollout, Use of MPC Methods, Computer ChessDimitri Bertsekas281 views·1 year ago
1:32:12Lecture 10, 2025; Aggregation Methods for Off-Line Training, Applications to POMDP and CybersecurityDimitri Bertsekas484 views·1 year ago
1:39:51Lecture 9, 2025; Rollout and Its Variants for Stochastic and Adaptive Control, Application to WordleDimitri Bertsekas350 views·1 year ago
1:25:35Lecture 8, 2025; GPT, HMM, and Markov chains Rollout variants for most likely sequence generationDimitri Bertsekas1.2K views·1 year ago
48:52New Directions in RL TD(lambda), aggregation, seminorm projections, free-form sampling (from 2014)Dimitri Bertsekas679 views·1 year ago
2:00:51Lecture 7, 2025, Case studies Multi-robot warehouse, data associationDimitri Bertsekas474 views·1 year ago
1:24:41Lecture 6, 2025, Multistep Approximation in Value Space, Constrained Rollout, Multiagent RolloutDimitri Bertsekas757 views·1 year ago
1:50:32Lecture 4, 2025, POMDP, Systems with Changing Parameters, Adaptive Control, Model Predictive ControlDimitri Bertsekas982 views·1 year ago
1:25:24Lecture 3, 2025, LQ Problems, Approximation in Value Space, VI, and PI, Newton's Method, ExamplesDimitri Bertsekas1.2K views·1 year ago
36:17Computer chess with model predictive control and reinforcement learningDimitri Bertsekas1.7K views·1 year ago
2:06:50Lecture 2, 2025, Stochastic finite and infinite horizon DP, approximation in value and policy spaceDimitri Bertsekas2.3K views·1 year ago
2:04:16Lecture 1, 2025, Course overview RL and DP, AlphaZero, deterministic DP, examples, applicationsDimitri Bertsekas7.9K views·1 year ago
54:31Plenary lecture at IFAC Nonlinear MPC, 2024; Model Predictive Control and Reinforcement LearningDimitri Bertsekas5.1K views·1 year ago
1:59:20Lecture 1, 2024, course overview RL and DP, AlphaZero, discrete and continuous applicationsDimitri Bertsekas5.2K views·2 years ago
1:21:08Lecture 13 2024 Approximate LP. Approximation in policy space, policy gradient methods. EpilogueDimitri Bertsekas623 views·2 years ago
1:29:29Lecture 12 2024; Off-line training with neural nets for approximate VI and PI. AggregationDimitri Bertsekas405 views·2 years ago
1:38:04Lecture 11, 2024 On-line training, neural networks, and other approximation architecturesDimitri Bertsekas585 views·2 years ago
1:43:23Lecture 10, 2024; GPT, HMM, and Markov chains Rollout variants for most likely sequence generationDimitri Bertsekas2.1K views·2 years ago
1:10:09Lecture 9, 2024, Bayesian optimization and adaptive control with a POMDP approach. Wordle case studyDimitri Bertsekas3.0K views·2 years ago
16:42Acceptance remarks by Dimitri Bertsekas for 2014 Khachiyan Prize of the INFORMS Optimization SocietyDimitri Bertsekas481 views·2 years ago
1:32:39Lecture 8, 2024, Rollout for stochastic DP. Value space approx for infinite state and control spacesDimitri Bertsekas529 views·2 years ago
2:07:39Lecture 7, 2024, Case studies Multi-robot warehouse, multiagent routing, data associationDimitri Bertsekas642 views·2 years ago
1:27:03Lecture 6, 2024, Multistep Approximation in Value Space, Constrained Rollout, Multiagent RolloutDimitri Bertsekas587 views·2 years ago
1:30:28Lecture 5, 2024, Deterministic Rollout, cost improvement, sequential improvement, multiagent rolloutDimitri Bertsekas722 views·2 years ago
1:46:26Lecture 4, 2024, POMDP, Systems with Changing Parameters, Adaptive Control, Model Predictive ControlDimitri Bertsekas797 views·2 years ago
48:15Polyhedral Approximation Algorithms for Convex Optimization, NIPS 2008Dimitri Bertsekas508 views·2 years ago