marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...
IEEE

Deep Q-Learning with Gradient Target Tracking

Abstract: This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternative to the ...
IEEE

A Maximum Trusted Distance-Based Greedy Q-Learning Routing Strategy for LEO Satellite Networks

Abstract: Large-scale low Earth orbit (LEO) satellite networks are characterized by massive node populations, rapid topology changes, and resource-constrained individual satellites. Existing routing ...