Algorithms

Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Safe offline reinforcement learning is a promising way to bypass risky online interactions towards safe policy learning. Most existing …
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Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning
Semi-supervised learning holds great promise for many real-world applications, due to its ability to leverage both unlabeled and …
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A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement …
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Data-Driven Decision-Making Algorithms
Developing high-performance, robust, generalizable, and deployable data-driven decision-making algorithms for real-world problems.
Data-Driven Decision-Making Algorithms
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of …
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Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets …
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Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning …
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PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises …
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Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Based on the IVR framework, we further propose two practical algorithms, Sparse Q-learning (SQL) and Exponential Q-learning (EQL), which adopt the same value regularization used in existing works, but in a complete in-sample manner.
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Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
DOGE marries dataset geometry with deep function approximators in offline RL, and enables exploitation in generalizable OOD areas rather than strictly constraining policy within data distribution.
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When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning