Algorithms

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
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
An Efficient Multi-Agent Optimization Approach for Coordinated Massive MIMO Beamforming
Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming …
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Mind the Gap: Offline Policy Optimization for Imperfect Rewards
This paper proposes an offline policy optimization approach for imperfect rewards. Abstract: Reward function is essential in …
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Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization
Offline reinforcement learning (RL) that learns policies from offline datasets without environment interaction has received …
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When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps, while also simultaneously allowing learning from a fixed real-world dataset.
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When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning