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Found 11 papers, 1 papers with code
Simulating the economic impact of rationality through reinforcement learning and agent-based modelling
We find that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?
We make an assumption that LLMs can be used as implicit computational models of humans, and propose a framework to use synthetic demonstrations derived from LLMs to model subrational behaviors that are characteristic of humans (e. g., myopic behavior or preference for risk aversion).
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
INTAGS: Interactive Agent-Guided Simulation
To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning.
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
The recent advancements in Deep Learning (DL) research have notably influenced the finance sector.
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Limit order books are a fundamental and widespread market mechanism.
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly.
A$^2$-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems
For this reason, we propose a novel A$^2$-UAV framework to optimize the number of correctly executed tasks at the edge.
Learning to simulate realistic limit order book markets from data as a World Agent
Our approach proposes to learn a unique "world" agent from historical data.
Towards Realistic Market Simulations: a Generative Adversarial Networks Approach
Unfortunately, this approach does not capture the market response to the experimental agents' actions.
