Attaining sustainable development involves a mounting role in modern innovations
especially in the mining industry. It is anticipated that the world's need for minerals will …

Although deep reinforcement learning has recently been very successful at learning
complex behaviors, it requires a tremendous amount of data to learn a task. One of the …

Decision-making is a dynamic process requiring perception, memory, and reasoning to
make choices and find optimal policies. Traditional approaches to decision-making suffer …

Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement
learning (RL) algorithms due to the inefficiency in collecting successful experiences. Recent …

We consider a multi-operator multi-access edge computing (MEC) network for applications
with dependent tasks. Each task includes jobs executed based on logical precedence …

Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data
usage remains a central challenge to be tackled for its successful real-world applications. In …

近年来, 深度强化学习(deep reinforcement learning, DRL) 已经在诸多序贯决策任务中取得
瞩目成功, 但当前深度强化学习的成功很大程度依赖于海量的学习数据与计算资源 …

Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to
learn new tasks quickly. However, most meta-RL algorithms show poor generalization in …

Balancing oneself using the spine is a physiological alignment of the body posture in the
most efficient manner by the muscular forces for mammals. For this reason, we can see …

Fast adaptation to new tasks is extremely important for embodied agents in the real world.
Meta-reinforcement learning (meta-RL) has emerged as an effective method to enable fast …