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Found 15 papers, 8 papers with code
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
As a result, leveraging large observational datasets becomes a more attractive option for achieving high-quality policy learning.
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applications.
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy.
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy.
Conformal Off-policy Prediction
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment.
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications.
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process.
Policy Evaluation for Temporal and/or Spatial Dependent Experiments
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments.
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
In this paper we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time.
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Order dispatch is one of the central problems to ride-sharing platforms.
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.
Optimization and Control Applications
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets
We conduct a wide range of experiments to demonstrate the interpretability of causal attention, the effectiveness of various model components, and the time efficiency of our CausalTrans.
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
Sure Screening for Gaussian Graphical Models
We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting.
