Reinforcement Learning
Deterministic, Scalable, and Ready for the Real World
- Built To Order
- Production Ready
Reinforcement Learning (RL), despite its popularity in academic and gaming contexts, has failed to deliver meaningful impact in real-world industrial settings. Why? Because it’s fundamentally compute-prohibitive.
In complex environments, the number of samples required to learn optimal policies grows exponentially—often tending toward infinity. This makes traditional RL infeasible for dynamic, real-world problems where states, actions, and rewards aren’t limited or well-defined.
Its most celebrated successes—playing Go, Chess, or Atari games—occur in artificially constrained problem spaces. Beyond that, RL remains largely impractical for production-grade applications.
At Automatski, we’ve rebuilt RL from the ground up using deterministic algorithms. Our approach eliminates the need for massive sampling, reducing sample complexity from exponential to modest polynomial levels.
This enables reinforcement learning systems that are efficient, scalable, and deployable in real-world industrial domains—robotics, control systems, adaptive logistics, dynamic pricing, and more.
