Reinforcement Learning (RL) is a learning framework in which an agent learns a policy from continual interaction with the environment. A policy is a mapping from states to actions. The agent receives rewards as feedback on the actions performed. The objective of RL is to design autonomous agents...

Šio tyrimo objektas yra akcijų rinka, kuri suvokiama kaip kompleksinė sistema, sudaryta iš bazinių elementų (vertybinių popierių, prekybos infrastruktūros ir atomistinių heterogeninių investuotojų) ir procesų (prognozavimo, investicinių sprendimų priėmimo, finansinių sąskaitų...

The main object of this study is the stock market, seen as a complex system constituted of basic elements (securities, trading infrastructure and atomistic heterogeneous investors) and process flows (forecasting, investment decision making, trade execution, maintenance of financial records,...

While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of applications developed, there has been very little rigorous empirical evaluation of the performance and limitations of these algorithms. This paper proposes standard methods for such...

Players may categorize the strategies available to them. In many games there are different ways to categorize one's strategies (different frames) and which ones players use has implications for the outcomes realized. This paper proposes a model of agents who learn which frames to use through...

Players may categorize the strategies available to them. In many games there are different ways to categorize one's strategies (different frames) and which ones players use has implications for the outcomes realized. This paper proposes a model of agents who learn which frames to use through...

Large, macroeconomic shocks in the past have been shown to influence economic decisions in the present. We study in an experiment with 743 subjects whether small-scale, seemingly negligible, events also affect the formation of risk preferences. In line with a reinforcement learning model, we...

Large, macroeconomic shocks in the past have been shown to influence economic decisions in the present. We study in an experiment with 743 subjects whether small-scale, seemingly negligible, events also affect the formation of risk preferences. In line with a reinforcement learning model, we...

In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with...

Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks...