# The intrinsic motivation of reinforcement and imitation learning for sequential tasks

arXiv: https://arxiv.org/abs/2412.20573 · HTML: https://mlsanity.com/paper/2412.20573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 5 of its 183 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)
- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 5 of 5)

- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [FeUdal Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1703.01161/) (2017)
- [Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2006.11485/) (2020)
- [CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments](https://mlsanity.com/paper/1901.09720/) (2019)
- [Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis](https://mlsanity.com/paper/2309.07675/) (2023)

## Built on by (most cited first, 2 of 2)

- [Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration](https://mlsanity.com/paper/2512.04453/) (2025)
- [An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.23615/) (2025)

## Abstract (arXiv)

This work in the field of developmental cognitive robotics aims to devise a new domain bridging between reinforcement learning and imitation learning, with a model of the intrinsic motivation for learning agents to learn with guidance from tutors multiple tasks, including sequential tasks. The main contribution has been to propose a common formulation of intrinsic motivation based on empirical progress for a learning agent to choose automatically its learning curriculum by actively choosing its learning strategy for simple or sequential tasks: which task to learn, between autonomous exploration or imitation learning, between low-level actions or task decomposition, between several tutors. The originality is to design a learner that benefits not only passively from data provided by tutors, but to actively choose when to request tutoring and what and whom to ask. The learner is thus more robust to the quality of the tutoring and learns faster with fewer demonstrations. We developed the framework of socially guided intrinsic motivation with machine learning algorithms to learn multiple tasks by taking advantage of the generalisability properties of human demonstrations in a passive manner or in an active manner through requests of demonstrations from the best tutor for simple and composing subtasks. The latter relies on a representation of subtask composition proposed for a construction process, which should be refined by representations used for observational processes of analysing human movements and activities of daily living. With the outlook of a language-like communication with the tutor, we investigated the emergence of a symbolic representation of the continuous sensorimotor space and of tasks using intrinsic motivation. We proposed within the reinforcement learning framework, a reward function for interacting with tutors for automatic curriculum learning in multi-task learning.