🔥 Implementing DQN (Deep Q Learning) using PyTorch | CartPole Task From Gymnasium

🚀 Learn how to build a Deep Q Network (DQN) from scratch using PyTorch and train an AI agent to solve the CartPole environment with Gymnasium! In this tutorial, we’ll go step-by-step through the core concepts of Reinforcement Learning, including Q-Learning, Experience Replay, Epsilon-Greedy exploration, and target networks.

By the end of this video, you’ll understand how DQN works under the hood and how to implement it practically using PyTorch.

🔥 What You’ll Learn:

Understanding the CartPole environment
What is Deep Q Learning (DQN)?
Neural Networks for Q-value prediction
Experience Replay Buffer
Epsilon-Greedy Strategy
Target Networks in DQN
Training loop implementation in PyTorch

🛠️ Tech Stack:

Python
PyTorch
Gymnasium
NumPy
Matplotlib

🎯 This video is perfect for:

Beginners in Reinforcement Learning
Deep Learning enthusiasts
PyTorch learners
AI/ML students and researchers
Anyone curious about training agents with RL

📌 Reinforcement Learning is one of the most exciting fields in AI, powering systems behind robotics, gaming AI, autonomous agents, and more. This tutorial gives you a strong practical foundation to start building your own RL projects.

#DeepLearning #ReinforcementLearning #DQN #PyTorch #MachineLearning #ArtificialIntelligence #Gymnasium #CartPole #Python #AI #RL #NeuralNetworks #DataScience #OpenAIGym #DeepQNetwork

Видео 🔥 Implementing DQN (Deep Q Learning) using PyTorch | CartPole Task From Gymnasium канала MLWorks