Autonomous Reinforcement Learning of Multiple Interrelated Tasks
This addresses the challenge of developing versatile artificial agents for complex environments, but it appears incremental as it builds on prior intrinsically motivated learning research.
The paper tackles the problem of autonomous learning of multiple interrelated tasks in robotics, proposing a method based on task selection within an MDP framework to maximize competence across all tasks.
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be interrelated (or "hierarchical") so that a robot has to first learn to achieve some of them to set the preconditions for learning other ones. Even though different strategies have been used in robotics to tackle the acquisition of interrelated tasks, in particular within the developmental robotics framework, autonomous learning in this kind of scenarios is still an open question. Building on previous research in the framework of intrinsically motivated open-ended learning, in this work we describe how this question can be addressed working on the level of task selection, in particular considering the multiple interrelated tasks scenario as an MDP where the system is trying to maximise its competence over all the tasks.