GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Cognitive and Developmental Systems Year : 2022

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

Abstract

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 stateof- 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.
Fichier principal
Vignette du fichier
GRIMGEP_arxiv.pdf (5.92 Mo) Télécharger le fichier
GRIMGEP_TCDS_final (1).pdf (3.36 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03121352 , version 1 (26-01-2021)
hal-03121352 , version 2 (22-11-2021)

Identifiers

Cite

Grgur Kovač, Adrien Laversanne-Finot, Pierre-Yves Oudeyer. GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning. IEEE Transactions on Cognitive and Developmental Systems, 2022, ⟨10.1109/TCDS.2022.3216911⟩. ⟨hal-03121352v2⟩

Collections

INRIA INRIA2
101 View
161 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More