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Showing 1 to 9 of 9 for “"Offline Reinforcement Learning"”.
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Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents
Performance of state-of-the art offline and model-based reinforcement learning (RL) algorithms deteriorates significantly when subjected to severe data scarcity and the presence of heterogeneous agents. In this work, we propose a model-based offline RL method to approach this setting. Using all …
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PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Latent Factor Representation
Offline reinforcement learning, where a policy is learned from a fixed dataset of trajectories without further interaction with the environment, is one of the greatest challenges in reinforcement learning. Despite its compelling application to large, real-world datasets, existing RL benchmarks have …
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Batch value function tournament for offline policy selection in reinforcement learning
Offline policy selection is a challenging open problem in reinforcement learning that has many important applications. The recently proposed Batch Value Function Tournament (BVFT) algorithm for batch learning offers some nice properties and can be applied to the model selection problem. In this …
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Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
… ranging from 2 D to 20 D. Our approach leverages offline reinforcement learning on large-scale optimization trajectories collected from 12 BO variants. To scale pretraining, we generate millions of synthetic Gaussian process-based functions with diverse landscapes, enabling the model to learn …
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Quasimetric decision transformer: enhancing goal-conditioned reinforcement learning with structured distance guidance
Recent works have shown that tackling offline Reinforcement Learning (RL) with a conditional policy produces promising results. Decision Transformer (DT) have shown promising results in offline RL by leveraging sequence modeling. However, standard DTs rely on Returns-to-Go (RTG) tokens, which are …
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Subdominance Minimization: A Satisficing Perspective on Imitation Learning
… objectives. However, prevailing imitation learning methods tend to prioritize optimizing a single imitation objective. This myopic focus on a singular objective frequently leads to unintended and undesirable behaviors in learned models. For example, an autonomous vehicle prioritizing travel …
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Statistical Limits and Efficient Algorithms for Learning-Enabled Control
As the use of large-scale learning for control continues to grow, the development of sample-efficient algorithms becomes increasingly critical. However, even in the simplest settings, we often do not know algorithms which achieve optimal sample complexity with respect to particular problem …
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Towards simulator enabled offline validation and learning of reinforcement learning agents
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Aligning Machine Learning and Robust Decision-Making
Machine learning (ML) has become increasingly ubiquitous across many applications worldwide, ranging from areas like supply chain to personalized pricing, recommendations, and more. These predictive models are often used as tools to inform operations and decision-making, with the potential to …