Learn Reinforcement Learning
Master the art of training agents that learn by interacting with environments. From Q-learning and policy gradients to deep RL algorithms like DQN, PPO, and A3C - all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Reinforcement Learning? Agents, environments, rewards, and the RL loop explained.
2. Core Concepts
Markov Decision Processes, states, actions, policies, value functions, and Bellman equations.
3. Value Methods
Q-learning, SARSA, temporal difference learning, and Monte Carlo methods.
4. Policy Methods
Policy gradient theorem, REINFORCE, actor-critic methods, and advantage estimation.
5. Deep RL
DQN, PPO, A3C, SAC, and combining deep learning with reinforcement learning.
6. Applications
Game playing, robotics, recommendation systems, and OpenAI Gym environments.
7. Best Practices
Reward shaping, hyperparameter tuning, debugging RL, reproducibility, and deployment.
What You'll Learn
By the end of this course, you will be able to:
Understand RL Foundations
Grasp the agent-environment loop, MDPs, policies, value functions, and the exploration-exploitation tradeoff.
Implement Classic Algorithms
Build Q-learning, SARSA, and policy gradient agents from scratch with Python code.
Train Deep RL Agents
Use DQN, PPO, and A3C to train agents that play games and control robots.
Apply RL in Practice
Work with OpenAI Gym, Stable Baselines3, and deploy RL models to real-world tasks.
Go Deeper With Expert Courses
Recommended learning resources from our partners. Affiliate disclosure.
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