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Intrinsically motivated graph exploration using network theories of human curiosity

2023-07-11 · Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro, Kieran A. Murphy, Dani S. Bassett

Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to longer exploratory walks and larger environments than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that next-node recommendations considering curiosity are more predictive of human choices than PageRank centrality in several real-world graph environments.

📄 PDF Abstract BibTeX arXiv:2307.04962

Code (1)

spatank/curiosity-graphs 공식 구현 pytorch

Tasks

Graph Neural Network Recommendation Systems reinforcement-learning Reinforcement Learning

Deep Intrinsically Motivated Exploration in Continuous Control

2022-10-01 · Baturay Saglam, Suleyman S. Kozat

In continuous control, exploration is often performed through undirected strategies in which parameters of the networks or selected actions are perturbed by random noise. Although the deep setting of undirected explorati…

continuous-control Continuous Control Deep Reinforcement Learning reinforcement-learning+2

Is GPT4 a Good Trader?

2023-09-20 · Bingzhe Wu

Recently, large language models (LLMs), particularly GPT-4, have demonstrated significant capabilities in various planning and reasoning tasks \cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there…

Prompt Engineering

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

2017-08-07 · Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer

Intrinsically motivated spontaneous exploration is a key enabler of autonomous developmental learning in human children. It enables the discovery of skill repertoires through autotelic learning, i.e. the self-generation,…

Developmental Learning Multi-Goal Reinforcement Learning Reinforcement Learning Self-Supervised Learning

Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration

2021-07-19 · ICML Workshop URL 2021 7 · Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V. Albrecht

Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters. In this work,…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Intrinsically-Motivated Reinforcement Learning: A Brief Introduction

2022-03-03 · Mingqi Yuan

Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufac…

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1