Beyond advanced mathematics or theoretical computing breakthroughs, PQC is about protecting the systems enterprises already ...

This suite implements several model-free off-policy deep reinforcement learning algorithms for discrete and continuous action spaces in PyTorch. DQN Single Discrete Mnih et. al. 2015 Double DQN Single ...

Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and decision-making systems across industries. Modern RL ...

This week in Project52, I took on one of the most exciting challenges yet: a direct face-off between two of the most powerful reinforcement learning (RL) algorithms — Deep Q-Network (DQN) and Proximal ...

Abstract: Various artificial intelligence (AI) algorithms have been developed for autonomous vehicles (AVs) to support environmental perception, decision making and automated driving in real-world ...

Terahertz (THz) communications have been regarded as a promising candidate for future wireless communication systems, to support the explosive growth of mobile devices, with ultra-high bandwidth and ...

Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The ...

Abstract: Modeling collective behavior is a way to better understand the mechanisms that govern collective animal behaviors. Traditional rule-based modeling methods rely heavily on human prior ...

Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been ...

Forest management can be seen as a sequential decision-making problem to determine an optimal scheduling policy, e.g., harvest, thinning, or do-nothing, that can mitigate the risks of wildfire. Markov ...