Joint Learning of Hierarchical Neural Options and Abstract World Model

• URL: http://arxiv.org/abs/2602.02799v1
• Date: Mon, 02 Feb 2026 20:58:11 GMT
• Title: Joint Learning of Hierarchical Neural Options and Abstract World Model
• Authors: Wasu Top Piriyakulkij, Wolfgang Lehrach, Kevin Ellis, Kevin Murphy,
• Abstract summary: We investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options.<n>We propose a novel method, which jointly learns an abstract world model and a set of hierarchical neural options.<n>We show, on a subset of Object-Centric Atari games, that our method can learn more skills using much less data than baseline methods.
• Score: 16.909000091644682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options. However, existing model-free hierarchical reinforcement algorithms need a lot of data. We propose a novel method, which we call AgentOWL (Option and World model Learning Agent), that jointly learns -- in a sample efficient way -- an abstract world model (abstracting across both states and time) and a set of hierarchical neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using much less data than baseline methods.

Related papers

• A Survey on Generative Recommendation: Data, Model, and Tasks [55.36322811257545]
  generative recommendation reconceptualizes recommendation as a generation task rather than discriminative scoring.<n>This survey provides a comprehensive examination through a unified tripartite framework spanning data, model, and task dimensions.<n>We identify five key advantages: world knowledge integration, natural language understanding, reasoning capabilities, scaling laws, and creative generation.
  arXiv  Detail & Related papers  (2025-10-31T04:02:58Z)
• Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems [0.6267574471145215]
  We propose a novel active learning framework with significant potential for application in modern AI systems.<n>Unlike the traditional active learning methods, which only focus on determining which data point should be labeled, our framework also introduces an innovative perspective on incorporating different query scheme.<n>Our proposed active learning framework exhibits higher accuracy and lower loss compared to other methods.
  arXiv  Detail & Related papers  (2024-12-31T05:12:51Z)
• Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction [19.59151245929067]
  We study whether giving an agent an object-centric mapping (describing a set of items and their attributes) allow for more efficient learning.<n>We find this problem is best solved hierarchically by modelling items at a higher level of state abstraction to pixels, and attribute change at a higher level of temporal abstraction to primitive actions.<n>We propose a fully model-based algorithm that learns a discriminative world model, plans to explore efficiently with only a count-based intrinsic reward, and can subsequently plan to reach any discovered (abstract) states.
  arXiv  Detail & Related papers  (2024-08-21T17:59:31Z)
• Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery [47.28191501836041]
  In this paper, we employ a Reinforcement Learning framework to simulate the cognitive processes of humans. We also deploy a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets.
  arXiv  Detail & Related papers  (2023-08-26T07:55:32Z)
• Towards A Unified Agent with Foundation Models [18.558328028366816]
  We investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets.
  arXiv  Detail & Related papers  (2023-07-18T22:37:30Z)
• Implicit Offline Reinforcement Learning via Supervised Learning [83.8241505499762]
  Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. We show how implicit models can leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets.
  arXiv  Detail & Related papers  (2022-10-21T21:59:42Z)
• Neural Routing in Meta Learning [9.070747377130472]
  We aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks. In this work, we describe an approach that investigates task-dependent dynamic neuron selection in deep convolutional neural networks (CNNs) by leveraging the scaling factor in the batch normalization layer. We find that the proposed approach, neural routing in meta learning (NRML), outperforms one of the well-known existing meta learning baselines on few-shot classification tasks.
  arXiv  Detail & Related papers  (2022-10-14T16:31:24Z)
• Hierarchical Skills for Efficient Exploration [70.62309286348057]
  In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. Prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. We propose a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-20T22:29:32Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• Model-free Representation Learning and Exploration in Low-rank MDPs [64.72023662543363]
  We present the first model-free representation learning algorithms for low rank MDPs. Key algorithmic contribution is a new minimax representation learning objective. Result can accommodate general function approximation to scale to complex environments.
  arXiv  Detail & Related papers  (2021-02-14T00:06:54Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.