Resource Constrained Deep Reinforcement Learning

by   Abhinav Bhatia, et al.

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Systems); vehicles (cars, bikes, scooters etc.) have to be matched to docking stations so as to reduce lost demand in shared mobility systems. Such problem domains are challenging owing to the demand uncertainty, combinatorial action spaces (due to allocation) and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions). Existing systems typically employ myopic and greedy optimization approaches to optimize allocation of supply resources to locations. Such approaches typically are unable to handle surges or variances in demand patterns well. Recent research has demonstrated the ability of Deep RL methods in adapting well to highly uncertain environments. However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources. To that end, we have developed three approaches on top of the well known actor critic approach, DDPG (Deep Deterministic Policy Gradient) that are able to handle constraints on resource allocation. More importantly, we demonstrate that they are able to outperform leading approaches on simulators validated on semi-real and real data sets.


page 1

page 2

page 3

page 4


Hierarchical Planning for Resource Allocation in Emergency Response Systems

A classical problem in city-scale cyber-physical systems (CPS) is resour...

Game-Theoretic Model Based Resource Allocation During Floods

For multiple emergencies caused by natural disasters, it is crucial to a...

Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites

In the coming years, the satellite broadband market will experience sign...

Newsvendor Model with Deep Reinforcement Learning

I present a deep reinforcement learning (RL) solution to the mathematica...

Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario

The Network Slicing (NS) paradigm enables the partition of physical and ...

Graph Reinforcement Learning for Predictive Power Allocation to Mobile Users

Allocating resources with future channels can save resource to ensure qu...

Please sign up or login with your details

Forgot password? Click here to reset