UsefulLinks
Computer Science
Artificial Intelligence
Deep Learning
Reinforcement Learning
1. Foundations of Reinforcement Learning
2. Mathematical Foundations
3. Markov Decision Processes
4. Dynamic Programming
5. Monte Carlo Methods
6. Temporal-Difference Learning
7. Function Approximation
8. Deep Reinforcement Learning
9. Policy Gradient Methods
10. Advanced Topics
11. Implementation and Practical Considerations
12. Applications and Case Studies
7.
Function Approximation
7.1.
Need for Function Approximation
7.1.1.
Large State Spaces
7.1.2.
Continuous State Spaces
7.1.3.
Curse of Dimensionality
7.1.4.
Generalization Requirements
7.2.
Value Function Approximation
7.2.1.
Approximate Value Functions
7.2.2.
Function Approximation Architectures
7.2.3.
Feature Representation
7.2.3.1.
Hand-Crafted Features
7.2.3.2.
Basis Functions
7.2.3.3.
Feature Selection
7.2.4.
Linear Function Approximation
7.2.4.1.
Linear Combinations of Features
7.2.4.2.
Weight Vector Learning
7.2.4.3.
Convergence Properties
7.2.5.
Nonlinear Function Approximation
7.2.5.1.
Neural Networks
7.2.5.2.
Decision Trees
7.2.5.3.
Kernel Methods
7.3.
Prediction with Function Approximation
7.3.1.
Gradient-Based Methods
7.3.1.1.
Stochastic Gradient Descent
7.3.1.2.
Learning Rate Schedules
7.3.1.3.
Convergence Analysis
7.3.2.
Semi-Gradient Methods
7.3.2.1.
Bootstrapping with Function Approximation
7.3.2.2.
Stability Issues
7.3.2.3.
Convergence Conditions
7.3.3.
Least-Squares Methods
7.3.3.1.
LSTD (Least-Squares TD)
7.3.3.2.
Computational Complexity
7.3.3.3.
Batch Updates
7.4.
Control with Function Approximation
7.4.1.
Action-Value Function Approximation
7.4.2.
Semi-Gradient SARSA
7.4.3.
Semi-Gradient Q-Learning
7.4.4.
Policy Gradient Methods
7.5.
Stability and Convergence Issues
7.5.1.
The Deadly Triad
7.5.1.1.
Function Approximation
7.5.1.2.
Bootstrapping
7.5.1.3.
Off-Policy Learning
7.5.2.
Divergence Examples
7.5.3.
Stabilization Techniques
7.6.
Feature Construction
7.6.1.
Tile Coding
7.6.2.
Radial Basis Functions
7.6.3.
Fourier Basis
7.6.4.
Polynomial Features
Previous
6. Temporal-Difference Learning
Go to top
Next
8. Deep Reinforcement Learning