Efficient Unsupervised Environment Design through Hierarchical Policy Representation Learning

• URL: http://arxiv.org/abs/2602.09813v1
• Date: Tue, 10 Feb 2026 14:19:40 GMT
• Title: Efficient Unsupervised Environment Design through Hierarchical Policy Representation Learning
• Authors: Dexun Li, Sidney Tio, Pradeep Varakantham,
• Abstract summary: Unsupervised Environment Design (UED) has emerged as a promising approach to developing general-purpose agents through automated curriculum.<n>We introduce a hierarchical Markov Decision Process (MDP) framework for environment design.<n>We show that our method outperforms baseline approaches while requiring fewer teacher-student interactions in a single episode.
• Score: 28.99712640511788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised Environment Design (UED) has emerged as a promising approach to developing general-purpose agents through automated curriculum generation. Popular UED methods focus on Open-Endedness, where teacher algorithms rely on stochastic processes for infinite generation of useful environments. This assumption becomes impractical in resource-constrained scenarios where teacher-student interaction opportunities are limited. To address this challenge, we introduce a hierarchical Markov Decision Process (MDP) framework for environment design. Our framework features a teacher agent that leverages student policy representations derived from discovered evaluation environments, enabling it to generate training environments based on the student's capabilities. To improve efficiency, we incorporate a generative model that augments the teacher's training dataset with synthetic data, reducing the need for teacher-student interactions. In experiments across several domains, we show that our method outperforms baseline approaches while requiring fewer teacher-student interactions in a single episode. The results suggest the applicability of our approach in settings where training opportunities are limited.

Related papers

• Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback [59.287761696290865]
  We propose a computationally efficient algorithm that achieves a sublinear regret guarantee for contextual episodic Markov Decision Processes (MDPs) with personalized feedback.<n>We demonstrate the effectiveness of our method in learning personalized objectives from multi-turn interactions through experiments on both a synthetic episodic MDP and a real-world user booking dataset.
  arXiv  Detail & Related papers  (2026-02-09T06:29:54Z)
• UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models [59.693733170193944]
  Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings.<n>Recent reinforcement learning approaches address this limitation but face two critical challenges.<n>We propose the Unidirectional Cognitive Optimization (UCO) method to address these challenges.
  arXiv  Detail & Related papers  (2025-11-12T01:27:02Z)
• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
  Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-12T17:35:43Z)
• Improving Environment Novelty Quantification for Effective Unsupervised Environment Design [7.973747521623636]
  Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent.<n>Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance.<n>This paper introduces the Coverage-based Evaluation of Novelty In Environment (CENIE) framework.
  arXiv  Detail & Related papers  (2025-02-08T23:59:41Z)
• DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback [62.235925602004535]
  DataEnvGym is a testbed of teacher environments for data generation agents.<n>It frames data generation as a sequential decision-making task, involving an agent and a data generation engine.<n>Students are iteratively trained and evaluated on generated data, and their feedback is reported to the agent after each iteration.
  arXiv  Detail & Related papers  (2024-10-08T17:20:37Z)
• Certifiably Robust Policies for Uncertain Parametric Environments [57.2416302384766]
  We propose a framework based on parametric Markov decision processes (MDPs) with unknown distributions over parameters.<n>We learn and analyse IMDPs for a set of unknown sample environments induced by parameters.<n>We show that our approach produces tight bounds on a policy's performance with high confidence.
  arXiv  Detail & Related papers  (2024-08-06T10:48:15Z)
• Learning Curricula in Open-Ended Worlds [17.138779075998084]
  This thesis develops a class of methods called Unsupervised Environment Design (UED) Given an environment design space, UED automatically generates an infinite sequence or curriculum of training environments. The findings in this thesis show that UED autocurricula can produce RL agents exhibiting significantly improved robustness.
  arXiv  Detail & Related papers  (2023-12-03T16:44:00Z)
• Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling [8.256433006393243]
  We introduce a hierarchical MDP framework for environment design under resource constraints. It consists of an upper-level RL teacher agent that generates suitable training environments for a lower-level student agent. Our proposed method significantly reduces the resource-intensive interactions between agents and environments.
  arXiv  Detail & Related papers  (2023-09-30T08:21:32Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Diversity Induced Environment Design via Self-Play [9.172096093540357]
  We propose a task-agnostic method to identify observed/hidden states that are representative of a given level. The outcome of this method is then utilized to characterize the diversity between two levels, which as we show can be crucial to effective performance. In addition, to improve sampling efficiency, we incorporate the self-play technique that allows the environment generator to automatically generate environments that are of great benefit to the training agent.
  arXiv  Detail & Related papers  (2023-02-04T07:31:36Z)
• Learning Multi-Objective Curricula for Deep Reinforcement Learning [55.27879754113767]
  Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL) In this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum.
  arXiv  Detail & Related papers  (2021-10-06T19:30:25Z)
• Human AI interaction loop training: New approach for interactive reinforcement learning [0.0]
  Reinforcement Learning (RL) in various decision-making tasks of machine learning provides effective results with an agent learning from a stand-alone reward function. RL presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. Imitation Learning (IL) offers a promising solution for those challenges using a teacher.
  arXiv  Detail & Related papers  (2020-03-09T15:27:48Z)

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.