This paper discusses the use of portfolio approaches based on bandit algorithms to optimize
multicriteria decision-making in recommender systems (accuracy and diversity). While …

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in
complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a …

Proximal policy optimization (PPO) is a recent advancement in reinforcement learning,
which is formulated as an unconstrained optimization problem including two terms …

With the advancements in wireless sensor networks and the Internet of Underwater Things
(IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which …

This paper presents a Double Deep Q-Network algorithm for trading single assets, namely
the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for …

Multi-objective reinforcement learning (MORL) is increasingly relevant due to its
resemblance to real-world scenarios requiring trade-offs between multiple objectives …

Episodic memory enables animals to map contexts and environmental features in space and
time but is underused in artificial intelligence (AI). Here we show how simple associative …

This paper studies the trans formative role of Reinforcement Learning for Requirements
Engineering in the context of software development. The integration of Reinforcement …

The harsh and dynamic lunar surface environment, developing autonomous lunar landing
has proven to be a difficult problem for space agencies. Reinforcement learning (RL) has …

Elasticity is an essential treatment in Cloudenvironment employed in academic and
industrial contexts. The main purpose of elasticity is to reduce thedeployment cost while …