A visual explanation of reinforcement learning

A visual explanation demonstrates how reinforcement learning enables artificial agents to develop behaviors through trial and error, similar to how humans learn. Using an interactive spark creature with a simple four-neuron brain, the article shows how adjusting neural connections (represented as three parameters) and receiving rewards for successful light-seeking behavior teaches the agent to improve its performance over repeated trials. This process mirrors the learning algorithm underlying reinforcement learning, where random adjustments combined with feedback gradually shape an agent toward optimal behavior.