# GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.04388 · HTML: https://mlsanity.com/paper/2008.04388/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [OMNI: Open-endedness via Models of human Notions of Interestingness](https://mlsanity.com/paper/2306.01711/) (2023)
- [An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey](https://mlsanity.com/paper/2209.08890/) (2022)
- [TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL](https://mlsanity.com/paper/2103.09815/) (2021)
- [Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning](https://mlsanity.com/paper/2104.05043/) (2021)
- [Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning](https://mlsanity.com/paper/2110.12997/) (2021)
- [DisTop: Discovering a Topological representation to learn diverse and rewarding skills](https://mlsanity.com/paper/2106.03853/) (2021)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals](https://mlsanity.com/paper/2601.19810/) (2026)

## Abstract (arXiv)

Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to predefined goal features or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend those concepts to unsupervised image-based goal exploration. We propose a framework that allows agents to autonomously identify and ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic forgetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches. We construct a rich 3D image based environment with distractors. Experiments on this environment show that agents using our framework successfully identify interesting regions of the environment, resulting in drastically improved performances. The source code is available at https://sites.google.com/view/grimgep.