Reinforcement Learning: A Primer
Understand the basics of RL, agents, environments, and rewards. A complete introduction to building autonomous systems.
Introduction to Reinforcement Learning

Reinforcement Learning (RL) is a paradigm of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Unlike supervised learning, where the model is provided with the correct answer key, an RL agent must learn from its own experience through trial and error.
Imagine teaching a robot to walk. You don't program the specific angles of each joint for every millisecond. Instead, you create a system where the robot receives a positive "reward" for moving forward and a negative "reward" for falling over. Over millions of attempts, the robot learns the complex coordination required to walk, run, and even jump.
Core Concepts
The Agent
The entity that makes decisions. It perceives the state of the world and acts upon it.
The Environment
The world in which the agent lives and interacts. It responds to the agent's actions and provides new states.
Formally, the problem is often modeled as a Markov Decision Process (MDP). At each time step:
- State (S)The current situation or configuration of the environment.
- Action (A)What the agent chooses to do.
- Reward (R)Immediate feedback signal from the environment (scalar value).
- Next State (S')The new situation after the action is taken.
The RL Loop
Exploration vs. Exploitation
One of the fundamental challenges in RL is the trade-off between exploration and exploitation.
Exploration
Trying new things that might lead to better rewards in the future, even if they don't seem optimal right now. "Taking a new route home."
Exploitation
Using current knowledge to maximize immediate reward. "Taking the known fastest route home."
Deep Reinforcement Learning
Traditional RL struggles with large state spaces (e.g., the number of pixel combinations in a video game). Deep RL solves this by using Neural Networks to approximate the value functions or policies.
Popular Algorithms
- DQN (Deep Q-Network)
- PPO (Proximal Policy Optimization)
- A3C (Asynchronous Advantage Actor-Critic)
- SAC (Soft Actor-Critic)
Applications
RL has moved from research labs to real-world impact:
- Robotics (manipulation, locomotion)
- Autonomous Vehicles (planning, control)
- Recommender Systems (optimizing engagement)
- Cooling Data Centers (Google DeepMind)
Start Building
The best way to learn RL is to build agents yourself. Start with simple environments like OpenAI Gym's CartPole, and work your way up to training agents for complex games or simulated robotics.
