UCL / DeepMind RL Lecture 3: Planning by Dynamic Programming — Policy & Value Iteration
Principal Research Scientist at DeepMind & Professor at UCL
1:39:09·2015·graduate level
Proves contraction convergence of iterative policy evaluation, policy iteration, value iteration, and generalized policy iteration.
Theory & AlgosSystems & CSAlgorithms & Data StructuresReinforcement LearningDynamic ProgrammingPolicy Iteration
#Reinforcement Learning Course by David Silver# Lecture 3: Planning by Dynamic Programming
#Slides and more info about the course: http://goo.gl/vUiyjq
Previous · Lecture 2
UCL / DeepMind RL Lecture 2: Markov Processes, Reward Processes & Bellman Equations
Next · Lecture 4UCL / DeepMind RL Lecture 4: Model-Free Prediction — Monte Carlo, TD(0) & TD(λ)
Seminar notes & discussion
No public seminar notes on this lecture yet.