🎯 Q-Learning Visualizer
Master temporal difference learning through interactive Q-value exploration
Your Progress
0 / 5 completedWhat is Q-Learning?
Learning Optimal Actions
Q-Learning is a model-free reinforcement learning algorithm that learns the quality (Q-value) of actions in different states. It discovers optimal policies by iteratively updating action-value estimates based on experience, without requiring a model of the environment.
Q-Value Function
Q(s,a) represents expected cumulative reward for taking action a in state s. The agent learns these values through experience.
Temporal Difference
Updates Q-values using the difference between predicted and actual rewards, enabling online learning.
Off-Policy Learning
Learns optimal policy while following exploratory behavior policy, separating learning from action.
Model-Free
No need to know environment dynamics—learns directly from interactions and observed rewards.
The Q-Learning Equation
Key Insight
Q-Learning bootstraps—it updates estimates using other estimates. This temporal difference approach allows learning before reaching terminal states, making it efficient for episodic and continuing tasks.
✅ Advantages
- •Simple and effective algorithm
- •Converges to optimal policy
- •Works with discrete state-action spaces
⚠️ Limitations
- •Doesn't scale to large state spaces
- •Slow convergence in complex environments
- •Requires careful exploration tuning