Symbolic regression, the task of predicting the mathematical expression of a function from
the observation of its values, is a difficult task which usually involves a two-step procedure …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and
engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics …

Machine learning frameworks such as genetic programming and reinforcement learning
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …

Many real-world systems can be naturally described by mathematical formulas. The task of
automatically constructing formulas to fit observed data is called symbolic regression …

Incompleteness is one of the problematic data quality challenges in real-world machine
learning tasks. A large number of studies have been conducted for addressing this …

Learning efficient and interpretable policies has been a challenging task in reinforcement
learning (RL), particularly in the visual RL setting with complex scenes. While neural …

With the rapid development of global trade, ports and terminals are playing an increasingly
important role, and automatic guided vehicles (AGVs) have been used as the main carriers …

Deep reinforcement learning has met noticeable successes recently for a wide range of
control problems. However, this is typically based on thousands of weights and non …