# The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

arXiv: https://arxiv.org/abs/1707.03300 · HTML: https://mlsanity.com/paper/1707.03300/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation](https://mlsanity.com/paper/2211.14296/) (2022)
- [Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks](https://mlsanity.com/paper/2301.00051/) (2022)
- [Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning](https://mlsanity.com/paper/2112.08932/) (2021)
- [Reinforcement Learning Textbook](https://mlsanity.com/paper/2201.09746/) (2022)
- [Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks](https://mlsanity.com/paper/2201.11783/) (2022)

## Abstract (arXiv)

This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence, inspired by infant development and motivated by the desire to build flexible robot manipulators capable of many diverse behaviours. We show that the IU agent not only learns to solve many tasks simultaneously but it also learns faster than agents that target a single task at-a-time. In some cases, where the single task DDPG method completely fails, the IU agent successfully solves the task. To demonstrate this, we build a playroom environment using the MuJoCo physics engine, and introduce a grounded formal language to automatically generate tasks.