Robot Learning

Online Reinforcement Learning (Online RL)

Online reinforcement learning (online RL) is the classical RL setting in which an agent improves its policy while actively interacting with the environment, learning from experience gathered through its own exploration. On-policy methods like PPO and off-policy methods like SAC both operate in this regime. On real robots, online RL is constrained by wear, safety, and reset costs, which is why much of it runs in simulation.

Why it matters for physical AI

Direct trial-and-error remains the most general route to high-performance control, and sim-based online RL with domain randomization produced the standout results in legged locomotion and agile flight.