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Artificial Intelligence IV - Reinforcement Learning in Java

Name: Artificial Intelligence IV - Reinforcement Learning in Java
Price: 199.99 USD
Availability: InStock

About this course

The course walks through the mathematical foundation of reinforcement learning using Markov Decision Processes, then shows how to solve them with value iteration, policy iteration, and Q‑learning. It demonstrates a model‑free Q‑learning approach, applies it to classic path‑finding problems, and extends the method with neural networks for function approximation. All examples and code are written in Java, so you can see the algorithms in action and adapt them to your own projects.

55/100

CourseAsk score

What the provider tells you: 31/45
Who stands behind it: 8/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Implement Markov Decision Processes and compute optimal policies with value and policy iteration
Apply Q‑learning to solve reinforcement learning problems without a model
Integrate neural networks with Q‑learning for function approximation
Develop Java programs that use reinforcement learning for path‑finding tasks

Course objectives

Explain the core concepts and mathematics behind reinforcement learning
Compare model‑based and model‑free solution methods
Write Java code that implements key RL algorithms
Demonstrate how to train and evaluate Q‑learning agents

Artificial Intelligence #reinforcement learning #artificial intelligence #machine learning #java #neural networks #Q-learning #markov decision processes #value iteration #policy iteration #pathfinding algorithms #markov decision process #function approximation #java programming #pathfinding #model free learning #algorithm implementation

$199.99

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