Takayuki Semitsu

Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation

Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of samples to learn meaningful policies. In this letter, we