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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

This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics: Markov Decision Processes value-iteration and policy-iterationQ-learning fundamentalspathfinding algorithms with Q-learningQ-learning with neural networks

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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