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.
1 code implementation • 19 Jul 2023 • Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, Shuran Song
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.
no code implementations • 13 Oct 2022 • Nelson Vadori, Leo Ardon, Sumitra Ganesh, Thomas Spooner, Selim Amrouni, Jared Vann, Mengda Xu, Zeyu Zheng, Tucker Balch, Manuela Veloso
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 30 Sep 2022 • Mengda Xu, Manuela Veloso, Shuran Song
We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning.
no code implementations • 5 Jan 2022 • Mengda Xu, Sumitra Ganesh, Pranay Pasula
In this paper we consider settings in which the data distribution(task) shifts abruptly and the timing of these shifts are not known.
no code implementations • 13 Oct 2021 • Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective.
1 code implementation • 14 Nov 2019 • Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng, Prashant Reddy, Manuela Veloso
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk.