AI Programs

Reinforcement Learning: Theory to Practice

Build intelligent agents using Q-Learning, Deep Q-Networks, and Policy Gradient methods.

★★★★★ 4.70 (48 reviews)
134 students
60 hours
Advanced
Reinforcement Learning: Theory to Practice

About This Course

Learn Markov Decision Processes, Q-learning, SARSA, DQN, A3C, PPO, and build game-playing agents and real-world RL applications using OpenAI Gym and Stable-Baselines3.

What You'll Learn

  • Core AI/ML concepts
  • Hands-on real-world projects
  • Industry-standard tools
  • Career-ready skills

Instructor

S

Saurav Kumar

Expert in Computer Science with 10+ years of teaching experience