Uploaded January 2026 | Updated September 2026, 3 weeks ago

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Reinforcement learning (RL) is a technique that enables AI agents to learn complex tasks, like playing video games or controlling robots, through trial and error. Think of it like training a pet: an AI agent observes its environment (such as pixel values from a game screen or sensor readings from a robot), takes actions to affect that environment, and receives rewards based on the outcomes of those actions.
The agent uses a reward function designed by developers to evaluate its performance. Positive rewards encourage good behavior (like a robotic arm successfully picking up an object), while negative rewards discourage poor performance (like losing in a video game). Based on these rewards, the agent automatically updates its internal parameters (similar to training other machine learning models) and attempts the task again, ideally improving over time. Note that this might take hundreds or thousands of episodes!
If you would like to get started training your own RL agents, check out my Introduction to Reinforcement Learning video here: youtube.com/watch?v=3av8vozEczU
In it, I walk you through the basics of setting up a gymnasium virtual environment and training an agent with the Deep-Q Network algorithm.
#ai #machinelearning #reinforcementlearning #programming #robotics