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Build Smart AI with Reinforcement Learning

Master reinforcement learning and build AI agents that make autonomous decisions. Learn key concepts, algorithms, and real-world applications in dynamic.

Course Overview

This course explores the fundamentals of reinforcement learning (RL), including reward-based learning, policy optimization, and deep reinforcement learning using AI agents. Learn to design and train intelligent agents for gaming, robotics, and real-world applications.

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FAQ

Course curriculum

Requirment

  • Proficiency in Python

  • Basic understanding of machine learning

  • Familiarity with NumPy & Matplotlib

  • Google account for Google Colab

  • Basic math skills (probability, linear algebra)

Outcomes

  • Concepts like agents, environments, rewards, episodes, policies, value functions, and Markov Decision Processes (MDPs)

  • Including Q-Learning, SARSA, and Monte Carlo methods

  • Implement and compare policy gradient methods like REINFORCE and Actor-Critic

  • Learn the challenges of stability, convergence, and sample efficiency in RL

  • Use neural networks to approximate value functions (DQN, Double DQN, DDPG, PPO)

...

₹3499

₹4999
... Buy Now
  • ...

    language

     English
  • ...

    Duration

     00h 20m

  • Level

     beginner
  • ...

    Expiry period

     Lifetime
  • ...

    Certificate

     Yes
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