Our joint paper, with Romuald Elie and Carl Remlinger entitled Reinforcement Learning in Economics and Finance just appeared in Computational Economics, Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agen..
textabstractIn this article we describe reinforcement learning, a machine learning technique for sol...
Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a comp...
The construction of automatic Financial Trading Systems (FTSs) is a subject of research of high inte...
With Romuald Elie and Carl Remlinger we recently uploaded on ArXiv a paper on Reinforcement Learning...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
Imagine computer programs (agents) that learn to coordinate or to compete. This study investigates h...
This work presents a variety of reinforcement learning applications to the domain of nance. It com...
We propose to train trading systems by optimizing financial objec-tive functions via reinforcement l...
The rapid changes in the finance industry due to the increasing amount of data have revolutionized t...
Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a speci...
We propose to train trading systems by optimizing fi-nancial objective functions via reinforcement l...
Market exposed assets like stocks yield higher return than cash but have higher risk, while cash-equ...
this paper we are interested in agents with learning capabilities. In a very general sense, learning...
Reinforcement learning (RL) is a computational framework for sequential decision-making, which combi...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
textabstractIn this article we describe reinforcement learning, a machine learning technique for sol...
Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a comp...
The construction of automatic Financial Trading Systems (FTSs) is a subject of research of high inte...
With Romuald Elie and Carl Remlinger we recently uploaded on ArXiv a paper on Reinforcement Learning...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
Imagine computer programs (agents) that learn to coordinate or to compete. This study investigates h...
This work presents a variety of reinforcement learning applications to the domain of nance. It com...
We propose to train trading systems by optimizing financial objec-tive functions via reinforcement l...
The rapid changes in the finance industry due to the increasing amount of data have revolutionized t...
Repeated play in games by simple adaptive agents is investigated. The agents use Q-learning, a speci...
We propose to train trading systems by optimizing fi-nancial objective functions via reinforcement l...
Market exposed assets like stocks yield higher return than cash but have higher risk, while cash-equ...
this paper we are interested in agents with learning capabilities. In a very general sense, learning...
Reinforcement learning (RL) is a computational framework for sequential decision-making, which combi...
This paper surveys the eld of reinforcement learning from a computer-science per-spective. It is wri...
textabstractIn this article we describe reinforcement learning, a machine learning technique for sol...
Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a comp...
The construction of automatic Financial Trading Systems (FTSs) is a subject of research of high inte...