Robot Learning

Deep Reinforcement Learning

Deep Reinforcement Learning is the combination of reinforcement learning with deep neural network function approximators for policies, value functions, or world models. Landmark results include DQN on Atari, AlphaGo, and OpenAI's dexterous in-hand manipulation. In robotics it is most commonly applied in massively parallel simulation, with algorithms such as PPO and SAC, followed by transfer of the trained policy to hardware.

Why it matters for physical AI

Simulation-scale deep RL produces controllers, particularly for legged locomotion and dexterous manipulation, that are difficult to hand-engineer or demonstrate. Combined with domain randomization it is a primary route from GPU-hours to deployable low-level skills.