Covers: theory of Linear Function Approximation

- How to approximate the state and state-action value using Linear Function Approximation?

It's highly recommended to watch the full lecture video to understand the concept of function approximation in RL.

Fail to play? Open the link directly: https://www.youtube.com/watch?v=buptHUzDKcE

Prof. Emma Brunskill

0 comment

Contributors

- Objectives
- Introduces function approximators in Reinforcement Learning to obtain compact representation that generalizes across states and actions in the environment.
- Potential Use Cases
- Strategic Games, Robotics
- Who is This For ?
- ADVANCEDAnyone who is interested in learning the advanced concepts and applications of Reinforcement Learning Algorithms

Click on each of the following **annotated items** to see details.

Resources6/6

PAPER 1. Playing Atari with Deep Reinforcement Learning

- What is Deep Q-learning?
- How Deep Q-Learning works?

30 minutes

VIDEO 2. CNNs and Deep Q Learning

- How to use Neural Networks as function approximators in Q-learning?

30 minutes

ARTICLE 3. Double Deep Q Networks

- What is Double Q-Learning?

20 minutes

PAPER 4. Deep Reinforcement Learning with Double Q-learning

- What is Double Q-Learning?

30 minutes

VIDEO 5. Value Function Approximation

- How to approximate the state and state-action value using Linear Function Approximation?

30 minutes

ARTICLE 6. Understanding Q-Learning and Linear Function Approximation

- How to approximate the value of state/action using Linear Function Approximation?

15 minutes

0 comment