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data Advanced 21 lessons

Reinforcement Learning

Build agents that learn by doing. Master Q-Learning, Policy Gradients, and train autonomous agents using OpenAI Gym environments.

Reinforcement Learning (RL) is how AI learns to play games and control robots. This course explores the agent-environment loop where algorithms learn from rewards and penalties. You will implement classical Q-Learning tables and advance to Deep Q-Networks (DQN) for complex tasks. Learn to use the Gymnasium (formerly OpenAI Gym) library to train agents to balance poles, drive cars, and play Atari games. This is the closest AI gets to 'learning' in the human sense.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
RL Team
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Complete Course Syllabus

  • 1
    RL Fundamentals
    Agents, environments, states, actions, and rewards.
  • 2
    Q-Learning
    Building a table-based agent to solve simple grids.
  • 3
    Deep Q-Networks
    Using neural networks to approximate Q-values.
  • 4
    Policy Gradients
    Optimizing policy functions directly for continuous actions.
  • 5
    Gymnasium Labs
    Training agents on CartPole and LunarLander environments.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$140k - $200k

Career Paths

RL Research Scientist $150k-$220k
Game AI Programmer $120k-$160k
Robotics Control Eng $130k-$180k

What You Will Learn

Build autonomous agents that learn strategies from trial and error
Implement Q-Learning and Deep Q-Networks (DQN) algorithms
Design custom reward functions to guide agent behavior
Train agents in complex environments using Gymnasium
Understand the exploration vs exploitation trade-off in AI

Skills You Will Gain

Reinforcement Learning Q-Learning Deep Q-Networks Reward Engineering PyTorch

Who Is This For

AI Researchers
Game Developers
Robotics Engineers

Prerequisites

Deep Learning Basics
Python

Reinforcement Learning FAQs

Game AI?

Yes, RL is heavily used in modern gaming agents.

Robots?

The logic applies directly to robotic control systems.

Is it stable?

RL training can be unstable; we teach tuning.

Math heavy?

Yes, involves probability and Bellman equations.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

Gymnasium
PyTorch
Python
NumPy
Jupyter

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