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Reinforcement Learning Foundations

via LinkedIn Learning

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Overview

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Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.

Syllabus

Introduction

Reinforcement learning in a nutshell

1. Getting Started with Reinforcement Learning

Terms in reinforcement learning
A basic RL problem
Markov decision process
A basic RL solution

2. Reinforcement Learning Algorithms

Monte Carlo method
Temporal difference methods
Other RL algorithms

3. Monte Carlo Method

The setting
Exploration and exploitation
Monte Carlo prediction
First visit and every visit MC prediction
Monte Carlo control
Additional modifications

4. Temporal Difference Methods

The setting
SARSA
SARSAMAX (Q-learning)
Expected SARSA

5. Modified Forms of Reinforcement

Deep reinforcement learning
Multi-agent reinforcement learning
Inverse reinforcement learning

Conclusion

Your reinforcement learning journey

Taught by

Khaulat Abdulhakeem

Reviews

4.5 rating at LinkedIn Learning based on 437 ratings

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