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Found 5 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.
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
Phantom -- A RL-driven multi-agent framework to model complex systems
Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions.
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets
We introduce a general technique to wrap a DEMAS simulator into the Gym framework.
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.
