← AI-aktiviteter Svenska

Q-learning simulation

Train an agent to navigate a grid with reinforcement learning – watch the Q-values update in real time.

Agent Goal Coin Obstacle
Episode: 0, Step: 0
Speed
Parameters – change any time, even during training
α learning rate
0.50
γ discount factor
0.95
ε exploration rate
0.90
How the agent chooses an action right now:
Exploration 90%Exploitation 10%
Step log – click a row for an explanation
Learning curve – number of steps to reach the goal per episode
Start training and the curve will be drawn: the number of steps per episode drops as the agent learns a shorter path.
Learn more – click for an explanation

Q-learning simulation

An agent moves through a grid and has to learn to collect all the coins and reach the goal – entirely on its own, just by trial and error and receiving rewards and penalties.

01
Train the agent – press Start training and the agent will try its way through the grid, over and over.
02
Follow the Q-values – the color and numbers on each cell show what the agent has learned so far about each action.
03
Adjust the parameters – change α, γ, and ε and see how it affects how fast and stably the agent learns.

Click the cards on the right for explanations, or click log rows and underlined words for more information.