Course
Deep Reinforcement Learning in Python
AdvancedSkill Level
Updated 09/2024PyTorchArtificial Intelligence4 hr15 videos49 Exercises4,050 XP5,342Statement of Accomplishment
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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!Prerequisites
Intermediate Deep Learning with PyTorchReinforcement Learning with Gymnasium in Python1
Introduction to Deep Reinforcement Learning
Discover how deep reinforcement learning improves upon traditional Reinforcement Learning while studying and implementing your first Deep Q Learning algorithm.
2
Deep Q-learning
Dive into Deep Q-learning by implementing the original DQN algorithm, featuring Experience Replay, epsilon-greediness and fixed Q-targets. Beyond DQN, you will then explore two fascinating extensions that improve the performance and stability of Deep Q-learning: Double DQN and Prioritized Experience Replay.
3
Introduction to Policy Gradient Methods
Learn about the foundational concepts of policy gradient methods found in DRL. You will begin with the policy gradient theorem, which forms the basis for these methods. Then, you will implement the REINFORCE algorithm, a powerful approach to learning policies. The chapter will then guide you through Actor-Critic methods, focusing on the Advantage Actor-Critic (A2C) algorithm, which combines the strengths of both policy gradient and value-based methods to enhance learning efficiency and stability.
4
Proximal Policy Optimization and DRL Tips
Explore Proximal Policy Optimization (PPO) for robust DRL performance. Next, you will examine using an entropy bonus in PPO, which encourages exploration by preventing premature convergence to deterministic policies. You'll also learn about batch updates in policy gradient methods. Finally, you will learn about hyperparameter optimization with Optuna, a powerful tool for optimizing performance in your DRL models.
Deep Reinforcement Learning in Python
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FAQs
What is Deep Reinforcement Learning (DRL)?
Deep Reinforcement Learning (DRL) combines reinforcement learning (RL) and deep learning. RL involves agents learning to make decisions by interacting with their environment, while deep learning involves neural networks with many layers (deep) that can learn representations of data with multiple levels of abstraction.
What real-world applications can Deep Reinforcement Learning (DRL) be used for?
DRL can be applied in various fields such as robotics, game AI, autonomous driving, finance, healthcare, and more. The skills you gain in this course will enable you to tackle complex problems in these domains. With the skills acquired from this course, you can pursue roles such as AI Engineer, Machine Learning Engineer, Data Scientist, Research Scientist, Robotics Engineer, and other positions that require expertise in reinforcement learning and deep learning.
Do I need a powerful computer to run the code in this course?
You can complete the course with a standard computer.
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