Function Approximation | Reinforcement Learning Part 5

Reinforcement Learning 7: Function approximation

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Function Approximation

Shimon Whiteson - Function Approximation and Deep Learning

On The Hardness of Reinforcement Learning With Value-Function Approximation

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Simon Du - Seminar - "On Reinforcement Learning with Large State Space and Long Horizon"

Zap Q-learning with Nonlinear Function Approximation, by Sean Meyn

A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous, Value-Based Reinforcement Learning A

RSS 2021, Spotlight Talk 83: Lyapunov-stable neural-network control

Prerequisites for the Deep Learning Specialization Math and Programming Background Explained

Linglong Kong: Exploration and Optimization in Deep Reinforcement Learning

CoinDICE: Off-Policy Confidence Interval Estimation via Dual Lens

Reinforcement Learning via an Optimization Lens

Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms

Humanoid Reinforcement Learning: Perturbation Test

Optimality and Approximation with Policy Gradient Methods

Last-Iterate Convergence in Constrained Min-Max Optimization: SOS to the Rescue

TILOS Seminar: On Policy Optimization Methods for Control (2022-09-28)

Stochastic Approximation and Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms