Course description
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
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Training content
Master the Fundamentals of Deep Reinforcement Learning
Our journey begins with the foundations of DRL and their relationship to traditional Reinforcement Learning. From there, we swiftly move on to implementing Deep Q-Networks (DQN) in PyTorch, including advanced refinements such as Double DQN and Prioritized Experience Replay to supercharge your models.
Take your skills to the next level as you explore policy-based methods. You will learn and implement essential policy-gradient techniques such as REINFORCE and Actor-Critic methods.
Use Cutting-edge Algorithms
You will encounter powerful DRL algorithms commonly used in the industry today, including Proximal Policy Optimization (PPO). You will gain practical experience with the techniques driving breakthroughs in robotics, game AI, and beyond. Finally, you will learn to optimize your models using Optuna for hyperparameter tuning.
By the end of this course, you will have acquired the skills to apply these cutting-edge techniques to real-world problems and harness DRL's full potential!
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Datacamp
DataCamp offers a comprehensive platform for learning data skills, specializing in training individuals and teams in data science, analytics, and AI. With a focus on interactive, hands-on learning, DataCamp provides courses across key programming languages such as Python, R, SQL,...
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