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

Lecture 5, 2025, Deterministic Rollout and Animations

Lecture 12, 2025; Training of cost functions, approximation in policy space, policy gradient methods

Convert Any Current Graph to Charge Graph (Step-by-Step + Intuition)

Lecture 8, 2025; GPT, HMM, and Markov chains: Rollout variants for most likely sequence generation

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

Controlled Reach-Avoid Set Computation for Discrete-Time Polynomial Systems via Convex Optimization

Reinforcement Learning Course at ASU

Reinforcement Learning, Model Predictive Control, and the Newton Step for Solving Bellman's Equation

Lecture 10, 2025; Aggregation Methods for Off-Line Training, Applications to POMDP and Cybersecurity

Bertsekas - Dynamic Programming

Abstract Dynamic Programming, Reinforcement Learning, Newton's Method, and Gradient Optimization

Неужели не ясно, что картина вовсе не о девушке?

Lecture 7, 2025, Case studies: Multi-robot warehouse, data association

Lec 01. Introduction to Deep Learning

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

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

Лучший документальный фильм про создание ИИ

Computer chess with model predictive control and reinforcement learning

Lecture 6, 2025, Multistep Approximation in Value Space, Constrained Rollout, Multiagent Rollout

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