1 citations · 1 across the 11 of their papers we have counts for
12 papers
OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items
Shuze Daniel Liu, David Simchi-Levi, Claire Chen +2
Modern supply chain operations can require coordinating replenishment across thousands of heterogeneous items under correlated stochastic demand, heterogeneous lead times, and shar…
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
Ethan Blaser, Jiuqi Wang, Shangtong Zhang
The average reward is a fundamental performance metric in reinforcement learning (RL) focusing on the long-run performance of an agent. Differential temporal difference (TD) learni…
Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic
Zeyu Mu, Shangtong Zhang, B. Brian Park
Connected automated vehicles (CAVs) possess the ability to communicate and coordinate with one another, enabling cooperative platooning that enhances both energy efficiency and tra…
MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability
Tie Ma, Yixi Chen, Vaastav Anand +8
We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified int…
Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach
Shangtong Zhang
In this paper, we formalize the almost sure convergence of -learning and linear temporal difference (TD) learning with Markovian samples using the Lean 4 theorem prover based on…
CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening
Amar Kulkarni, Shangtong Zhang, Madhur Behl
Ensuring the safety of autonomous vehicles (AVs) requires identifying rare but critical failure cases that on-road testing alone cannot discover. High-fidelity simulations provide…
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
Xiaochi Qian, Zixuan Xie, Xinyu Liu +1
This paper establishes the first almost sure convergence rate and the first maximal concentration bound with exponential tails for general contractive stochastic approximation algo…
Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning
Claire Chen, Shuze Daniel Liu, Shangtong Zhang
In reinforcement learning, classic on-policy evaluation methods often suffer from high variance and require massive online data to attain the desired accuracy. Previous studies att…
Doubly Optimal Policy Evaluation for Reinforcement Learning
Shuze Daniel Liu, Claire Chen, Shangtong Zhang
Policy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due to the sequential na…
Efficient Multi-Policy Evaluation for Reinforcement Learning
Shuze Daniel Liu, Claire Chen, Shangtong Zhang
To unbiasedly evaluate multiple target policies, the dominant approach among RL practitioners is to run and evaluate each target policy separately. However, this evaluation method…
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang
Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e.g., stochastic gradient descent and temporal dif…
Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
Shuze Liu, Shuze Daniel Liu, Shangtong Zhang +2
Online Monte Carlo evaluation is a fundamental tool for assessing policy performance in reinforcement learning and sequential decision-making problems arising in operations researc…