Search Results for author:
Found 7 papers, 2 papers with code
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems.
XSkill: Cross Embodiment Skill Discovery
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
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
ASPiRe:Adaptive Skill Priors for Reinforcement Learning
We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning.
Mixture of basis for interpretable continual learning with distribution shifts
In this paper we consider settings in which the data distribution(task) shifts abruptly and the timing of these shifts are not known.
Towards a fully RL-based Market Simulator
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.
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
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.
