FeaturedUdemy
Deep Reinforcement Learning in Python
4.8(275)5k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
Deep Reinforcement Learning (DRL) drives state-of-the-art developments in robotics, gaming AI, and autonomous control. This advanced course provides hands-on code development using PyTorch and Gymnasium, covering value-based methods, policy gradients, state-of-the-art algorithms like PPO, and hyperparameter tuning with Optuna.
Who It's For
Advanced AI engineers, reinforcement learning researchers, and PyTorch developers building autonomous decision-making agents in simulated environments.
Key Takeaways
- Value-Based Algorithms: Implement Deep Q-Networks (DQN), Double DQN, and Prioritized Experience Replay within Gymnasium environments.
- Policy Gradients & Actor-Critic: Master policy-based RL techniques including REINFORCE, Actor-Critic methods, and Proximal Policy Optimization (PPO).
- Optimization & Tuning: Apply hyperparameter optimization using Optuna to tune complex DRL models for stability and convergence.
About this course
Discover the cutting-edge techniques that empower machines to learn and interact with their environments. You will dive into the world of Deep Reinforcement Learning (DRL) and gain hands-on experience with the most powerful algorithms driving the field forward. You will use PyTorch and the Gymnasium environment to build your own agents.
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!Advertisement
![Data Analysis Real world use-cases- Hands on Python [2026]](/_next/image?url=https%3A%2F%2Fimg-c.udemycdn.com%2Fcourse%2F750x422%2F3819580_9b6d.jpg&w=3840&q=75)