Learning to Solve Tasks with Exploring Prior Behaviours

Understand

Demonstrations are widely used in Deep Reinforcement Learning (DRL) for facilitating solving tasks with sparse rewards.

• However, the tasks in real-world scenarios can often have varied initial conditions from the demonstration, which would require additional prior behaviours.
• For example, consider we are given the demonstration for the task of \emph{picking up an object from an open drawer}, but the drawer is closed in the training.
• Without acquiring the prior behaviours of opening the drawer, the robot is unlikely to solve the task.