Introduction to Approximate Solution Methods for Reinforcement Learning

Reinforcement learning (RL) requires approximate solution methods to handle problems with large state spaces where tabular methods fail. This approach uses a parameterized function with a weight vector to represent value functions, effectively turning the problem into a supervised learning task. Optimization is typically performed using Stochastic Gradient Descent (SGD) to minimize a prediction objective, such as the Mean Squared Value Error. The content covers both linear function approximation with feature construction and non-linear methods like neural networks for this purpose, introducing concepts like semi-gradient methods.