TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

• URL: http://arxiv.org/abs/2506.19997v2
• Date: Wed, 02 Jul 2025 10:44:04 GMT
• Title: TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
• Authors: Geonwoo Cho, Jaegyun Im, Jihwan Lee, Hojun Yi, Sejin Kim, Sundong Kim,
• Abstract summary: Generalizing deep reinforcement learning agents to unseen environments remains a significant challenge.<n>We present Transition-aware Regret Approximation with Co-learnability for Environment Design (TRACED)<n>TRACED yields curricula that improve zero-shot generalization across multiple benchmarks while requiring up to 2x fewer environment interactions than strong baselines.
• Score: 5.404569468550549
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalizing deep reinforcement learning agents to unseen environments remains a significant challenge. One promising solution is Unsupervised Environment Design (UED), a co-evolutionary framework in which a teacher adaptively generates tasks with high learning potential, while a student learns a robust policy from this evolving curriculum. Existing UED methods typically measure learning potential via regret, the gap between optimal and current performance, approximated solely by value-function loss. Building on these approaches, we introduce the transition prediction error as an additional term in our regret approximation. To capture how training on one task affects performance on others, we further propose a lightweight metric called co-learnability. By combining these two measures, we present Transition-aware Regret Approximation with Co-learnability for Environment Design (TRACED). Empirical evaluations show that TRACED yields curricula that improve zero-shot generalization across multiple benchmarks while requiring up to 2x fewer environment interactions than strong baselines. Ablation studies confirm that the transition prediction error drives rapid complexity ramp-up and that co-learnability delivers additional gains when paired with the transition prediction error. These results demonstrate how refined regret approximation and explicit modeling of task relationships can be leveraged for sample-efficient curriculum design in UED.

Related papers

• No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery [53.08822154199948]
  Unsupervised Environment Design (UED) methods have gained recent attention as their adaptive curricula promise to enable agents to be robust to in- and out-of-distribution tasks. This work investigates how existing UED methods select training environments, focusing on task prioritisation metrics. We develop a method that directly trains on scenarios with high learnability.
  arXiv  Detail & Related papers  (2024-08-27T14:31:54Z)
• Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning [2.3558144417896583]
  The planning problem constitutes a fundamental aspect of the autonomous driving framework. We propose Validity Learning on Failures, VL(on failure) as a remedy to address this issue. We show that VL(on failure) outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2024-06-03T17:25:18Z)
• Relaxed Contrastive Learning for Federated Learning [48.96253206661268]
  We propose a novel contrastive learning framework to address the challenges of data heterogeneity in federated learning. Our framework outperforms all existing federated learning approaches by huge margins on the standard benchmarks.
  arXiv  Detail & Related papers  (2024-01-10T04:55:24Z)
• Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality [55.88910947643436]
  Self-supervised pre-training is essential for handling vast quantities of unlabeled data in practice. HiDe-Prompt is an innovative approach that explicitly optimize the hierarchical components with an ensemble of task-specific prompts and statistics. Our experiments demonstrate the superior performance of HiDe-Prompt and its robustness to pre-training paradigms in continual learning.
  arXiv  Detail & Related papers  (2023-10-11T06:51:46Z)
• Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study [16.07380451502911]
  This paper revisits the robustness of the minimum error entropy criterion to deal with non-Gaussian noises. We investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common.
  arXiv  Detail & Related papers  (2023-07-17T15:38:11Z)
• Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice [54.03076395748459]
  A central question in the meta-learning literature is how to regularize to ensure generalization to unseen tasks. We present a generalization bound for meta-learning, which was first derived by Rothfuss et al. We provide a theoretical analysis and empirical case study under which conditions and to what extent these guarantees for meta-learning improve upon PAC-Bayesian per-task learning bounds.
  arXiv  Detail & Related papers  (2022-11-14T08:51:04Z)
• Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation [22.5935068122522]
  In this paper, we address the prediction accuracy problem of consistency learning methods with novel extensions of the mean-teacher (MT) model. The accurate prediction by this model allows us to use a challenging combination of network, input data and feature perturbations to improve the consistency learning generalisation. Results on public benchmarks show that our approach achieves remarkable improvements over the previous SOTA methods in the field.
  arXiv  Detail & Related papers  (2021-11-25T04:30:56Z)
• Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning [66.9937776799536]
  The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments. We propose a cross-modal grounding module to equip the agent with a better ability to track the correspondence between the textual and visual modalities.
  arXiv  Detail & Related papers  (2020-11-22T09:13:46Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)

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.