Data-Driven Decision-Making Algorithms

Last updated on Jun 25, 2024
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A main research direction for the AIR-DREAM Lab is to develop high-performance, robust, generalizable, and real-world deployable data-driven decision-making algorithms. We are specifically interested in offline policy learning methods, such as offline reinforcement learning (RL), offline imitation learning (IL), and offline planning, which enable a simulation-free and low-cost solution to address many real-world problems.

Our current research focus include:

  • Sample-efficient / high-generalization offline RL / IL / planning algorithms
  • Foundation models for decision-making
  • Safe offline RL algorithms
  • Hybrid RL that combines offline and online policy learning
  • Offline policy learning under imperfect reward
  • Feedback-efficient RLHF
Algorithms
Xianyuan Zhan
Xianyuan Zhan
Faculty Member

Publications

Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies
Hierarchical reinforcement learning (HRL) addresses complex long-horizon tasks by skillfully decomposing them into subgoals. Therefore, …
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Instruction-Guided Visual Masking
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment …
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Instruction-Guided Visual Masking
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a …
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Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously …
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DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Multimodal pretraining has emerged as an effective strategy for the trinity of goals of representation learning in autonomous robots: …
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Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) …
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A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, …
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A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Query-Policy Misalignment in Preference-Based Reinforcement Learning
Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents’ behavior with human desired outcomes, but is …
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Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
In this study, we investigate the DIstribution Correction Estimation (DICE) methods, an important line of work in offline reinforcement …
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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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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
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
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods …
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Discriminator-Guided Model-Based Offline Imitation Learning
Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward …
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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without …
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Model-Based Offline Planning with Trajectory Pruning
Offline reinforcement learning (RL) enables learning policies using pre-collected datasets without environment interaction, which …
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Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning
We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while …
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DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy …
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