Reinforcement Learning

Learning from trial and error — robots that optimize behavior from reward signals.

What Is Reinforcement Learning?

Reinforcement learning (RL) is a paradigm where an agent learns to maximize cumulative reward through interaction with an environment. The agent takes actions, receives rewards (or penalties), and updates its policy to improve over time.

Key Concepts

Reward signal — Sparse or dense feedback indicating task progress (e.g., object grasped, goal reached).
Policy — Mapping from observations to actions. Often neural networks (e.g., PPO, SAC).
Sim-to-real — Train in simulation, deploy on real robots. Domain randomization helps bridge the gap.

Related Resources

RL Environment as a Service — Real-world RL environments for your team
Sim-to-Real Transfer — Bridging simulation and reality
Data Services — Real-world data for RL fine-tuning