This approach is expected to be able to enhance the stability and performance of policy learning. We have conducted numerical experiments in various noise-prone ...

This approach is expected to be able to enhance the stability and performance of policy learning. We have conducted numerical experiments in various noise-prone ...

This method is designed to optimize the balance between exploration of new strategies and exploitation of known rewarding actions, aiming to diminish the volume ...

Nov 1, 2024 · Execution of most of the modern DPLL-based SAT solvers is guided by a number of heuristics. Decisions made during the search process are usually ...

May 30, 2024 · This approach is expected to be able to learn high-performing policies more stably. Numerical experiments are conducted in environments with ...

Oct 17, 2024 · This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor ...

This work develops a model free approach and develops a variation of Deep Q-Learning to estimate the optimal actions of a trader, which outperforms the ...

This application takes a model free approach and develops a variation of Deep Q-Learning to estimate the optimal actions of a trader.

A Deep Q-Network (DQN) is defined as a model that combines Q-learning with a deep CNN to train a network to approximate the value of the Q function, ...

In this paper, we develop realistic simulations of limit order markets and use them to design a high-frequency market making agent using. Deep Recurrent Q- ...