Interactive simulations demonstrating core RL concepts for Intelligent Transportation Systems (ITS) and Robotics.
Visualize how delayed rewards flow backward from a goal to the starting state using the Bellman Equation.
FoundationA Q-Learning agent managing a traffic intersection. Learn the trade-off between trying new things and sticking to what works.
Q-LearningCompare an optimistic agent (Q-Learning) against a conservative agent (SARSA) in a high-stakes environment.
SafetyExperiment with Sparse, Dense, and poorly designed reward functions to see how they impact learning speed.
Design