Frontiers

Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler

Genome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics. Although de novo assemblers have ...
GitHub

double-q-learning

Implemented Behavior Cloning, DAgger, Double Q-Learning, Dueling DQN, and Proximal Policy Optimization (PPO) in a simulated environment and analyzed/compared their performance in terms of efficiency, ...
Scientific Research Publishing

Kumar, A., Zhou, A., Tucker, G. and Levine, S. (2020) Conservative Q-Learning for Offline Reinforcement Learning. Advances in Neural Information Processing Systems, 33, 1179-1191.

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
GitHub

q-learning

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3 ...
Frontiers

Hybrid genetic algorithm and Q-learning-based solution for the time-variant berth and quay crane allocation problem

Institute of Logistics Science and Engineering of Shanghai Maritime University, Pudong, China Introduction: This study addresses the joint scheduling optimization of continuous berths and quay cranes ...
IEEE

Q-Learning Methods for LQR Control of Completely Unknown Discrete-Time Linear Systems

Abstract: This paper focuses on solving the linear quadratic regulator problem for discrete-time linear systems without knowing system matrices. The classical Q-learning methods for linear systems can ...