Transferring prior experience to new tasks is central to an agent's adaptability. In this work, we aim to accelerate online reinforcement learning by leveraging prior experience from large offline data.

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences. Our experiments demonstrate that ASPiRe can significantly accelerate the learning of new downstream tasks in the presence of multiple priors and show improvement on competitive baselines.

We find that the sample size has almost no impact on the learning. Notice that the target KL divergence imposes on Ant Maze is higher than the one on Point Maze. "space" to explore around the composite skill prior. As target KL divergence increases, the learned policy will receive less guidance from the prior. The algorithm is not sensitive to this parameter.

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences.

Real-world data often appears in diverse, disjoint forms -- with varying schemas, inconsistent semantics, and no fixed feature ordering -- making it challenging to build general-purpose models that can leverage information across datasets. We introduce ASPIRE, Arbitrary Set-based Permutation-Invariant Reasoning Engine, a Universal Neural Inference model for semantic reasoning and prediction over heterogeneous structured data. ASPIRE combines a permutation-invariant, set-based Transformer with a semantic grounding module that incorporates natural language descriptions, dataset metadata, and in-context examples to learn cross-dataset feature dependencies. This architecture allows ASPIRE to ingest arbitrary sets of feature--value pairs and support examples, align semantics across disjoint tables, and make predictions for any specified target. Once trained, ASPIRE generalizes to new inference tasks without additional tuning. In addition to delivering strong results across diverse benchmarks, ASPIRE naturally supports cost-aware active feature acquisition in an open-world setting, selecting informative features under test-time budget constraints for an arbitrary unseen dataset. These capabilities position ASPIRE as a step toward truly universal, semantics-aware inference over structured data.

In an era increasingly shaped by decentralized knowledge ecosystems and pervasive AI technologies, fostering sustainable learner agency has become a critical educational imperative. This paper introduces a novel conceptual framework integrating Generative Artificial Intelligence (GAI) and Learning Analytics (LA) to cultivate Self - Directed Growth -- a dynamic competency enabling learner s to iteratively drive their own developmental pathways across diverse contexts. Building upon critical gaps in current Self - Directed Learning (SDL) and AI - mediated educational research, the proposed Aspire to Potentials for Learners (A2PL) model reconcept ualizes the interplay of learner aspirations, complex thinking, and summative self - assessment within GAI - supported environments. Methodological implications for future intervention designs and data analytics are discussed, positioning Self - Directed Growth as a pivotal axis for designing equitable, adaptive, and sustainable learning systems in the digital era. 1. Introduction The educational realm faces two increasingly prominent challenges that threaten to reshape the landscape of learning and development . Firstly, the traditional teacher - dominated, institution - centered environment is being eclipsed by a decentralized, ever - evolving, and technologically advanced online landscape. In this new paradigm, knowledge and skills are not poised and delivered by a single expositor, but are constantly renewed, reproduced, and reiterated through sharing and co - creation, rendering existing models of education insufficient. And the overreliance on EdTech tools, as well as information search and synthesis tools, such as Generative Artificial Intelligence (GAI), among students poses a significant challenge in the contemporary educational landscape, while there is a concerning lack of research examining whether these tools genuinely foster the development of learner agency. The integration of AI into educational practices offers a transformative opportunity to enhance learning outcomes and promote equity. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), AI has the potential to acc elerate the achievement of Sustainable Development Goal 4 (SDG 4) by improving access to quality education for all learners, regardless of their socioeconomic background (UNESCO, 2019; UNESCO, 2021). As some noted, AI facilitates access to information and online education, helping to bridge the information, skill, and educational gaps faced by disadvantaged individuals who encounter barriers to traditional learning opportunities due to time constraints, financial limitations, geographic distance, or physic al challenges (Thakkar et al., 2020; Sanabria - Z et al., 2023).

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences.

This paper proposes an informative trajectory planning approach, namely, \textit{adaptive particle filter tree with sigma point-based mutual information reward approximation} (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.