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Safe RL: Constrained Exploration vs. Naive Exploration

Side-by-side reinforcement-learning race: a naive agent explores randomly and repeatedly falls off the table edge or crashes into obstacles, while a safe RL agent keeps a learned safety envelope and redirects risky actions into safe fallback moves, learning the same task with zero crashes.

Robotics & Kinematics3DAdvanced60 FPS

A side-by-side reinforcement-learning training race. Both robots learn the same grid-navigation task from scratch by trial and error. The naive agent explores unconstrained and racks up crashes from falling off the table edge and colliding with the obstacle before its policy improves. The safe agent maintains a learned safety envelope around hazards and reroutes any risky exploratory or greedy action into a safe fallback move, learning just as effectively with zero crashes.

⚙ Under the hood

A side-by-side reinforcement-learning training race: a naive robot explores fully at random and repeatedly falls off the table edge or crashes into an obstacle, its crash counter climbing fast, while a safe-RL robot keeps a visualized safety envelope around the same hazards and reroutes any risky action — exploratory or greedy — into a safe fallback move, learning just as effective a policy with a matching reward curve but zero crashes the entire run.

Three.jsSafe Reinforcement LearningConstrained RLSafety EnvelopeQ-LearningRobot Learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install