# Autonomous Reinforcement Learning of Multiple Interrelated Tasks

## TL;DR

This paper presents a method for autonomous reinforcement learning that enables agents to learn multiple interrelated tasks by modeling task selection as a Markov Decision Process aimed at maximizing overall competence.

## Contribution

It introduces a novel approach to autonomous multi-task learning by framing task selection as an MDP within intrinsically motivated open-ended learning.

## Key findings

- Effective learning of interrelated tasks demonstrated
- Competence maximization across multiple tasks achieved
- Framework applicable to complex, hierarchical environments

## Abstract

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.