AI concept
Q-learning is a reinforcement learning algorithm that learns action values from rewards without needing a model of the environment.
Computer Science AI Problem Lab
Maze Q-Learning Interactive Visualizer
Explore reinforcement learning by watching an agent move through a maze, collect rewards, update Q-values, and learn better paths. Learn the model, state transitions, search behavior, and practical AI reasoning flow through an OpenLabs interactive simulator.
AI Visualizer
States, actions, rewards, and Q-value updates
Q-Learning Maze
GA
Step 1
Observe state
Step 2
Choose action
Step 3
Receive reward
Step 4
Update Q-value
Reasoning flow
The agent explores states, takes actions, receives rewards, updates Q-values, and gradually improves its path toward the goal.
Model focus
States, actions, rewards, and Q-value updates
Visualization
Trace each state, decision, or rule step through an interactive learning flow.
Learn by simulating
Understand Q-Learning Maze with step-by-step AI reasoning
The agent explores states, takes actions, receives rewards, updates Q-values, and gradually improves its path toward the goal. The lab turns abstract AI problem solving into a visible sequence of states, decisions, and results.
Understand states, actions, rewards, and policies.
Visualize how Q-values change through experience.
Learn exploration versus exploitation in a maze.
Connect reinforcement learning with path-finding behavior.
Where this AI concept is used
- Robot navigation
- Game AI agents
- Path optimization
- Reinforcement learning practice
How the interactive lab works
Open the Q-Learning Maze lab, adjust the problem inputs, and follow how the visualizer updates each state or inference step. Use it to compare AI theory with observable behavior.
Curriculum Continuity
View All AI ProblemsExplore Sibling AI & Search Experiments
Variables, domains, and constraintsBacktracking search with constraint checking
Constraint Satisfaction Problem
Explore how AI solves constraint satisfaction problems by assigning values to variables while respecting rules and pruning invalid choices.
Rule-based AI inferenceFacts, rules, goals, and inference paths
Forward and Backward Chaining
Visualize rule-based reasoning as AI moves from facts to conclusions or works backward from a goal to required facts.
Search and guessing strategyState space search and probabilistic guessing
Hangman AI Problem
Study how an AI agent can reason about hidden words, update possible candidates, and choose letters based on available evidence.
Local search optimizationHeuristic optimization over neighboring states
Hill Climbing Search
Watch hill climbing move from one state to a better neighboring state until it reaches a peak or gets stuck in a local optimum.
Classical AI planningState-space planning and goal achievement
Monkey Banana Problem
Visualize how an AI planner solves the classic monkey banana problem by sequencing actions to reach a goal state.
State-space searchState transitions and goal search
Water Jug Problem
Solve the classic water jug problem by pouring, filling, and emptying jugs while tracing each state toward the target amount.
Deep learning & backpropagationForward pass activations, loss gradients, and weight updates
Multilayer Perceptron Neural Network
Interactive neural network simulator: configure layers, train on non-linear datasets, adjust learning rates, and visualize real-time decision boundary convergence.
Q-Learning Maze FAQs
What is Q-learning?
Q-learning is a reinforcement learning algorithm that learns the value of actions in states from rewards.
What is the maze agent learning?
The agent learns which actions lead to better rewards and shorter paths to the goal.
What are Q-values?
Q-values estimate how useful an action is in a given state for achieving future rewards.
Ready to explore Q-Learning Maze?
Launch the visualizer and turn AI search, reasoning, and planning into a clear hands-on learning path.