Reinforcement learning (RL)

A training method in which an agent learns by interacting with an environment (real or simulated) and maximising a reward signal. In robotics, it is used extensively in simulation (e.g. NVIDIA Isaac Lab) to produce control policies, which are then transferred to the physical robot (see: sim-to-real). Distinct from imitation learning: the agent does not need human demonstrations, but does require a well-defined reward function. EN: reinforcement learning (RL).