Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: A New Approach for Resource Scheduling with Deep Reinforcement Learning
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
Multi-Agent Reinforcement Learning Approaches for Distributed Job-Shop Scheduling Problems
Exploration-Exploitation Trade-off in Deep Reinforcement Learning
Multi-agent Deep Reinforcement Learning for Task Allocation in Dynamic Environment
Policy based reinforcement learning approach Of Jobshop scheduling with high level deadlock detection
A Human Mixed Strategy Approach to Deep Reinforcement Learning
Constraints-driven scheduling and resource assignment
User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach
Efficient Parallel Methods for Deep Reinforcement Learning
A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility
Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks
A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay
n ov 2 01 7 deep reinforcement learning for resource allocation in v 2 v communications .
SERF: Efficient Scheduling for Fast Deep Neural Network Serving via Judicious Parallelism
Spatial Deep Learning for Wireless Scheduling
Optimus: an efficient dynamic resource scheduler for deep learning clusters
Multi-Objective Deep Reinforcement Learning
M ULTI-TASK LEARNING WITH DEEP MODEL BASED REINFORCEMENT LEARNING
Rainbow: Combining Improvements in Deep Reinforcement Learning
Competitive Two-Level Adaptive Scheduling Using Resource Augmentation
Marriage, honesty, and stability
A Single-Layer Frequency-Selective Surface for Ultrawideband Electromagnetic Shielding
Muscle biopsy in Pompe disease.
Apprenticeship learning using linear programming
Highly Stretchable and Sensitive Photodetectors Based on Hybrid Graphene and Graphene Quantum Dots