Evolutionary Computation (EC) has been shown to be able to quickly train Deep Artificial
Neural Networks (DNNs) to solve Reinforcement Learning (RL) problems. While a Genetic …

Neuroevolution (ie, training neural network with Evolution Computation) has successfully
unfolded a range of challenging reinforcement learning (RL) tasks. However, existing …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Reinforcement Learning (RL) has achieved significant successes, which aims to obtain a
single policy maximizing the expected cumulative rewards for a given task. However, in …

Reinforcement learning (RL) problems often feature deceptive local optima, and methods
that optimize purely for reward often fail to learn strategies for overcoming them [2]. Deep …

Reinforcement learning has always been a research hotspot in the machine learning
community, which aims to model the process of investigating the interaction between agents …

Reinforcement learning has recently been recognized as a promising means of machine
learning, but its applicability remains limited in real-time environment due to its short …

We present RLLTE: a long-term evolution, extremely modular, and open-source framework
for reinforcement learning (RL) research and application. Beyond delivering top-notch …

While deep reinforcement learning systems have demonstrated impressive results in
domains ranging from game playing and robotic control, sample efficiency remains a major …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …