Action Priors for Large Action Spaces in Robotics
This work addresses the problem of expensive expert demonstration acquisition for robotic manipulation, offering an alternative for researchers and practitioners.
The paper introduces an action prior derived from solutions of previously solved tasks to aid exploration in new robotic manipulation problems. This method enables solving tasks that were previously infeasible without expert demonstrations.
In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert demonstrations can be expensive. This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks. The action prior is a probability distribution over actions that summarizes the set of policies found solving previous tasks. Our results indicate that this approach can be used to solve robotic manipulation problems that would otherwise be infeasible without expert demonstrations. Source code is available at \url{https://github.com/ondrejba/action_priors}.