Coursera MOOC / Non-credit 0

Deep Reinforcement Learning Hands-On

Name: Deep Reinforcement Learning Hands-On
Price: 49 USD
Availability: InStock

About this course

This specialization provides a comprehensive learning path in Deep Reinforcement Learning (RL), designed to equip learners with the necessary skills for practical applications. It begins by exploring foundational concepts in reinforcement learning, including core RL principles and the OpenAI Gym environment. Learners will also delve into deep learning using PyTorch and techniques like the Cross-Entropy Method and the Bellman Equation, with an introduction to advanced RL methods like Deep Q-Networks. By the end of the first course, learners will have a solid foundation in RL theory and practical skills. The second course takes learners deeper into advanced RL algorithms, such as DQN Extensions, Policy Gradients, and Actor-Critic Methods, covering applications like stock trading and chatbot training. The course emphasizes the practical use of RL to solve complex problems, helping learners master RL in various real-world contexts. The final course explores cutting-edge RL topics, including continuous action spaces, robotics, and the AlphaGo Zero algorithm. Learners will gain hands-on experience in advanced exploration techniques, multi-agent RL, and applying RL in discrete optimization problems. By the end of the specialization, learners will be well-versed in both foundational and advanced RL concepts, ready to tackle industry challenges.

75/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 20/35
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

understand foundational concepts of reinforcement learning
implement reinforcement learning algorithms using PyTorch
apply advanced methods like DQN Extensions and Actor-Critic Techniques
solve real-world problems using reinforcement learning applications

Course objectives

to equip learners with practical skills in reinforcement learning
to explore both foundational and advanced topics in deep reinforcement learning
to provide hands-on experience with relevant tools and methods

Machine Learning Artificial Intelligence #reinforcement learning #deep learning #pytorch #real-world applications #policy gradients #actor-critic methods #openai gym #dqn #multi-agent rl #advanced algorithms

$49.00

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