Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations

Understand

Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions.

• Recent developments in this area have demonstrated that using sparse rewards, i.e.
• rewarding the agent only when the task has been successfully completed, can lead to better policies.
• However, state-action space exploration is more difficult in this case.