no code implementations • 22 Oct 2023 • Yuchen Xiao, Yanchao Sun, Mengda Xu, Udari Madhushani, Jared Vann, Deepeka Garg, Sumitra Ganesh
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems.
no code implementations • 22 Jul 2023 • Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato
Bayesian reinforcement learning (BRL), thanks to its sample efficiency and ability to exploit prior knowledge, is uniquely positioned as such a solution method.
no code implementations • 10 Jan 2023 • Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng, Yuchen Xiao, Sumitra Ganesh
We propose a new algorithm, SAFFE, that makes fair allocations with respect to the entire demands revealed over the horizon by accounting for expected future demands at each arrival time.
no code implementations • 20 Sep 2022 • Yuchen Xiao
Empirical results demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions with macro-actions.
no code implementations • 20 Sep 2022 • Yuchen Xiao, Weihao Tan, Christopher Amato
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably.
no code implementations • 3 Jan 2022 • Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learning.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Oct 2021 • Yuchen Xiao, Xueguang Lyu, Christopher Amato
By using this local critic, each agent calculates a baseline to reduce variance on its policy gradient estimation, which results in an expected advantage action-value over other agents' choices that implicitly improves credit assignment.
Multi-agent Reinforcement Learning Policy Gradient Methods +2
no code implementations • 29 Sep 2021 • Yuchen Xiao, Weihao Tan, Christopher Amato
Many realistic multi-agent problems naturally require agents to be capable of performing asynchronously without waiting for other agents to terminate (e. g., multi-robot domains).
no code implementations • 8 Feb 2021 • Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato
Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community.
no code implementations • 27 Aug 2020 • Yuchen Xiao, Xiaosheng Zhuang
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set $K\subseteq \mathbb{R}^d$.
no code implementations • 18 Apr 2020 • Yuchen Xiao, Joshua Hoffman, Christopher Amato
In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations.
no code implementations • 19 Sep 2019 • Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato
In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control.
no code implementations • 17 Oct 2017 • Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan How
The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies.