Interactive Applications

Reinforcement Learning Trainer

This interactive demo showcases Q-learning, a fundamental reinforcement learning algorithm. The agent (🤖) learns to navigate through a grid environment to reach the target (🎯) while avoiding obstacles (🟫). The brightness of each cell represents the learned value of that state (brighter = higher value).

Controls: Adjust the learning rate and discount factor to see how they affect the learning process. The learning rate controls how quickly the agent incorporates new information, while the discount factor determines how much the agent values future rewards compared to immediate ones.