Post-Quantum Cryptography Is A Business Problem Hiding Inside Of A Math Problem

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

RL Dresden Algorithm Suite

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 ...
IEEE

An Explainable Q-Learning Method for Longitudinal Control of Autonomous Vehicles

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 ...
IEEE

Modeling Collective Behavior for Fish School With Deep Q-Networks

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 ...
Frontiers

Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning

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 ...
Frontiers

Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning

A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and ...