# Learning Task Agnostic Skills with Data-driven Guidance

arXiv: https://arxiv.org/abs/2108.01869 · HTML: https://mlsanity.com/paper/2108.01869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Variational Intrinsic Control](https://mlsanity.com/paper/1611.07507/) (2016)
- [Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills](https://mlsanity.com/paper/2002.03647/) (2020)
- [Empowerment -- an Introduction](https://mlsanity.com/paper/1310.1863/) (2013)

## Built on by (most cited first, 3 of 3)

- [Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired Skills](https://mlsanity.com/paper/2303.04592/) (2023)
- [Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment](https://mlsanity.com/paper/2501.17431/) (2025)
- [COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space](https://mlsanity.com/paper/2606.00950/) (2026)

## Abstract (arXiv)

To increase autonomy in reinforcement learning, agents need to learn useful behaviours without reliance on manually designed reward functions. To that end, skill discovery methods have been used to learn the intrinsic options available to an agent using task-agnostic objectives. However, without the guidance of task-specific rewards, emergent behaviours are generally useless due to the under-constrained problem of skill discovery in complex and high-dimensional spaces. This paper proposes a framework for guiding the skill discovery towards the subset of expert-visited states using a learned state projection. We apply our method in various reinforcement learning (RL) tasks and show that such a projection results in more useful behaviours.