In-Browser Reinforcement Learning

Definition

The execution and training of neural policy networks directly inside web browsers, coupling WebAssembly physics with WebGPU tensor operations for interactive agent learning.

Key Characteristics

• Real-time visual feedback of policy convergence and locomotion stability.
• User perturbation of environment parameters (gravity, friction, joint damping).
• Accessible deployment across platforms without server infrastructure.

Applications

• Interactive AI education
• Browser-based robotics prototyping

Mentions in Source

• “Direct ONNX / WebGPU neural policy inference driving simulated bipedal and quadrupedal robots.” — mjswan-mujoco-webassembly-simulation