This research paper presents a novel deep reinforcement learning (DRL) solution to the decision-making problem behind algorithmic trading in the stock markets: selecting the appropriate trading action (buy, hold or sell shares) without human intervention. Naturally, the core objective is to achieve an appreciable profit while efficiently mitigating the trading risk. This specific task is particularly complex due to the sequential nature of the problem as well as the stochastic and adversarial aspects of the environment. Moreover, a huge amount of both quantitative and qualitative information, which is generally not available, influences the dynamics of this environment. Until now, DRL algorithms mainly focused on well-known environment with specific properties, such as games.

Artificial Intelligence (AI) has enabled the development of high-performance automatic learning techniques in recent years. However, these techniques are often applied task by task, which implies that an intelligent agent trained for one task will perform poorly on other tasks, even very similar ones. To overcome this problem, researchers at the University of Liège (ULiège) have developed a new algorithm based on a biological mechanism called neuromodulation. This algorithm makes it possible to create intelligent agents capable of performing tasks not encountered during training. This novel and exceptional result is presented this week in the magazine PLOS ONE.

Artificial Intelligence (AI) has enabled the development of high-performance automatic learning techniques in recent years. However, these techniques are often applied task by task, which implies that an intelligent agent trained for one task will perform poorly on other tasks, even very similar ones. To overcome this problem, researchers at the University of Liège (ULiège) have developed a new algorithm based on a biological mechanism called neuromodulation. This algorithm makes it possible to create intelligent agents capable of performing tasks not encountered during training. This novel and exceptional result is presented this week in the magazine PLOS ONE.