Reverse curriculum generation for Reinforcement Learning

Work accepted at the Conference on Robot Learning (CoRL). Preprint available here:

Cite as: Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, Pieter Abbeel. Reverse Curriculum Generation for Reinforcement Learning. In Conference on Robot Learning (CoRL) 2017.


Many robotics tasks require a robot to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These tasks present considerable difficulties for reinforcement learning approaches, since the natural reward function for such goal-oriented tasks is sparse and prohibitive amounts of exploration are required to reach the goal and receive a learning signal. Past approaches tackle these problems by manually designing a task-specific reward shaping function to help guide the learning. Instead, we propose a method to learn these tasks without requiring any prior task knowledge other than obtaining a single state in which the task is achieved. The robot is trained in "reverse", gradually learning to reach the goal from a set of starting positions increasingly far from the goal. Our method automatically generates a curriculum of starting positions that adapts to the agent's performance, leading to efficient training on such tasks. We demonstrate our approach on difficult simulated fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.

Robotic manipulation experiments


Code available here.