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Covers: theory of Linear Function Approximation

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

This article explains the Linear Function Approximation and Q-Learning networks.

Daniel Seita

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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.

Resources

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

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