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With the advent of large datasets, offline reinforcement learning (ORL) is a promising framework for learning good decision-making policies without interacting...

Bibliographic details on Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning.

Offline reinforcement learning (offline RL) is increasingly approached as a sequence modeling task, with methods leveraging advanced architectures like...

Learning effective communication is one of the keys to improving coordination in multi-agent systems. This paper proposes a novel framework for offline...

Abstract page for arXiv paper 2410.11234v3: Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

Offline safe reinforcement learning (OSRL) involves learning a decision-making policy to maximize rewards from a fixed batch of training data to satisfy...

Cost-effective asset management is an area of interest across several industries. Specifically, this paper develops a deep reinforcement learning (DRL)...

Electronic proceedings of IJCAI 2024

Abstract page for arXiv paper 2512.03973v1: Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning

Statistical learning, the ability of the human brain to uncover patterns organized according to probabilistic relationships between elements and events of the...

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Abstract page for arXiv paper 2103.14077: Nearly Horizon-Free Offline Reinforcement Learning

In reinforcement learning, robust policies for high-stakes decision-making problems with limited data are usually computed by optimizing the percentile...

02/18/21 - Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algor...

NUS College implements 'Touch Grass Week' to encourage screen-free learning, fostering deeper engagement and critical thinking among undergraduates. Read more...

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In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications...

Children, especially from underserved communities & developing countries, need an offline digital learning solution that adapts to their needs

In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each...

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the...

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NUS College implements 'Touch Grass Week' to encourage screen-free learning, fostering deeper engagement and critical thinking among undergraduates. Read more...

Abstract page for arXiv paper 2103.14077v3: Nearly Horizon-Free Offline Reinforcement Learning

We study the problem of offline learning in automated decision systems under the contextual bandits model. We are given logged historical data consisting of...

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