Conventional hierarchical reinforcement learning (HRL) relies on discrete options to
represent explicitly distinguishable knowledge, which may lead to severe performance …

The aquaculture production sector is one of the suppliers of global food consumption needs.
Countries that have a large amount of water contribute to the needs of aquaculture …

One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is
sample efficiency. Many approaches learn a world model in order to train an agent entirely …

Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks.
However, extrinsic rewards frequently fall short in complex environments due to the …

Network coding is a potent technique extensively utilized in decentralized Internet of Things
(IoT) systems, including the Internet of Medical Things (IoMT). Nevertheless, the inherent …

Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely
on observing rewards to train the agent, and if informative rewards are sparse the agent …

Previous work has separately addressed different forms of action, state and action-state
entropy regularization, pure exploration and space occupation. These problems have …

Reinforcement learning can solve decision-making problems and train an agent to behave
in an environment according to a predesigned reward function. However, such an approach …

Curiosity appears to motivate and guide effective learning in humans, which has led to high
hopes in the machine learning community for machine analogues of curiosity. While a …

Novelty signals in the brain modulate learning and drive exploratory behaviors in humans
and animals. Inherently, whether a stimulus is novel or not depends on existing …