Deep Q-Learning in Action
Experience reinforcement learning as an AI agent learns to navigate through a dynamic environment in real-time
Try the Demo →Deep Q-Learning Explained
Deep Q-Learning combines deep neural networks with Q-learning to solve complex decision-making problems. Here's how it works:
- The AI maintains a Q-table of state-action values
- Deep neural networks approximate Q-values for unseen states
- Experience replay stabilizes learning
- ε-greedy strategy balances exploration and exploitation
Key Components
Neural Network Architecture
4-layer deep network with ReLU activation, processing 16 state variables to predict action values
Experience Replay
Stores and randomly samples past experiences to break behavioral correlations and improve learning stability
Target Network
Separate network for generating target Q-values, updated periodically to reduce overestimation
Technologies Used
TensorFlow.js
Deep learning in the browser, enabling real-time training and inference
Next.js 14
React framework with server components and app router
Tailwind CSS
Utility-first CSS for modern, responsive design
TypeScript
Type-safe development with better tooling support
Learning Process
The agent learns through these steps:
- State Observation:
Processes current game state including position, energy, and distances to objects
- Action Selection:
Uses ε-greedy strategy to choose between exploration and exploitation
- Reward Calculation:
Evaluates action outcomes based on energy changes and target proximity
- Network Update:
Adjusts neural network weights using gradient descent on the temporal difference error
Ready to See It in Action?
Watch as the AI learns from scratch and improves its strategy over time
Launch Demo →