Title: Reverse Curriculum Generation for Reinforcement Learning,
Abstract: Many relevant tasks require an agent to reach a certain state, or 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 goal-oriented tasks present a considerable challenge for
reinforcement learning, since their natural reward function is sparse and
prohibitive amounts of exploration are required to reach the goal and receive
some learning signal. Past approaches tackle these problems by exploiting
expert demonstrations or by manually designing a task-specific reward shaping
function to guide the learning agent. Instead, we propose a method to learn
these tasks without requiring any prior 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 start states increasingly far from the
goal. Our method automatically generates a curriculum of start states that
adapts to the agent's performance, leading to efficient training on
goal-oriented tasks. We demonstrate our approach on difficult simulated
navigation and fine-grained manipulation problems, not solvable by
state-of-the-art reinforcement learning methods.