Name: Reinforcement Learning for Business Lab
Author: MySimulator

Agent settings

Exploration rate (ε): 0.2

Five candidate prices exist, each with a hidden true expected reward (revenue per customer). The agent does not know these values and must learn them by trying prices and observing noisy sales outcomes, balancing exploring untested prices against exploiting the best one found so far.

Status

Rounds played0

Cumulative revenue—

Best price found—

Estimated value per price (bar height) vs. true value (dashed)