Reinforcement Learning Robot

Reinforcement learning for robotics is the application of RL algorithms to teach physical robots to perform tasks without explicit programming. An agent (neural network) observes the robot's state (joint positions, camera images, sensors) and outputs actions (motor commands). The environment provides reward signals based on task progress, and the agent learns a policy that maximizes cumulative reward. Classic applications: locomotion (walking, running), manipulation (grasping, assembly), navigation (obstacle avoidance), and dexterous control (multi-finger hands).