The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since
their first introduction in 2015. Though uses in many different applications are being found …

Despite the promising performance of deep reinforcement learning (DRL) agents in many
challenging scenarios, the black-box nature of these agents greatly limits their applications …

Score-based generative models like the diffusion model have been testified to be effective in
modeling multi-modal data from image generation to reinforcement learning (RL). However …

The advancement of robots, particularly those functioning in complex human-centric
environments, relies on control solutions that are driven by machine learning …

Domain-specific systems-on-chip (DSSoCs) combine general-purpose processors and
specialized hardware accelerators to improve performance and energy efficiency for a …

Building models of human decision-making from observed behaviour is critical to better
understand, diagnose and support real-world policies such as clinical care. As established …

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging
problems in various power system control scenarios. However, their decision-making …

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models
that are able to match the performance of state-of-the-art deep learning methods is one of …

Although it is widely acknowledged that reinforcement learning (RL) can be beneficial for
building control, many RL-based control actions remain unexplainable in the daily practice …

Deep reinforcement learning (DRL) has achieved impressive success in traffic signal control
systems (TSCS). However, since a key component of many DRL models is the complex …