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Deep Reinforcement Learning using python

Deep Reinforcement Learning using python

Learn to build intelligent agents that can make decisions in complex environments using Deep Reinforcement Learning (DRL). This course covers the fundamental concepts of DRL, including Q-learning, Policy Gradients, and actor-critic methods, with practical implementations in Python. You'll work with popular frameworks like TensorFlow and PyTorch to solve classic problems and real-world challenges.

₹349

₹3749
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Has discount
Expiry period Lifetime
Made in English
Last updated at Sun Aug 2025
Level
Beginner
Total lectures 63
Total quizzes 0
Total duration 10:46:53 Hours
Total enrolment 0
Number of reviews 0
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Short description Learn to build intelligent agents that can make decisions in complex environments using Deep Reinforcement Learning (DRL). This course covers the fundamental concepts of DRL, including Q-learning, Policy Gradients, and actor-critic methods, with practical implementations in Python. You'll work with popular frameworks like TensorFlow and PyTorch to solve classic problems and real-world challenges.
Outcomes
  • Understand DRL Principles: Comprehend the core theory and concepts of Reinforcement Learning and how deep learning is used to solve complex problems within this field.
  • Implement DRL Algorithms: Confidently implement key DRL algorithms from scratch in Python, including DQN, Policy Gradients (REINFORCE), and Actor-Critic methods like A2C and DDPG.
  • Apply DRL Frameworks: Effectively use major deep learning libraries like TensorFlow and PyTorch to build and train your DRL agents.
  • Solve Complex Problems: Design and deploy DRL agents to solve a variety of complex decision-making problems in simulated environments.
  • Develop Autonomous Systems: Lay the foundation for developing real-world autonomous systems in fields such as robotics, finance, and logistics.
  • Analyze and Debug DRL Models: Understand how to analyze the performance of your DRL agents and debug common issues encountered during training.
Requirements
  • Programming: Strong proficiency in Python, including a good understanding of data structures, algorithms, and object-oriented programming.
  • Mathematics: A solid grasp of linear algebra (vectors, matrices), calculus (derivatives, gradients), and basic probability theory
  • Machine Learning: Fundamental knowledge of machine learning concepts, including neural networks, supervised learning, and backpropagation.
  • Hardware: A computer with a good CPU is sufficient, but a dedicated GPU (e.g., NVIDIA) is highly recommended for faster training times, especially for more complex problems.

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