Advancements in reinforcement learning (RL) have demonstrated superhuman...
In large-scale multi-agent systems like taxi fleets, individual agents (...
Safety in goal directed Reinforcement Learning (RL) settings has typical...
The popularity of on-demand ride pooling is owing to the benefits offere...
Deep Reinforcement Learning (DRL) policies have been shown to be vulnera...
Recent work on designing an appropriate distribution of environments has...
Constrained Reinforcement Learning has been employed to enforce safety
c...
Agent decision making using Reinforcement Learning (RL) heavily relies o...
In multi-agent systems with large number of agents, typically the
contri...
Restless multi-armed bandits (RMAB) is a framework for allocating limite...
Restless Multi-Armed Bandits (RMAB) is an apt model to represent
decisio...
Owing to the benefits for customers (lower prices), drivers (higher
reve...
With increasing world population and expanded use of forests as cohabite...
The widespread availability of cell phones has enabled non-profits to de...
Influence maximization is the problem of finding a small subset of nodes...
In many public health settings, it is important for patients to adhere t...
In multi-capacity ridesharing, multiple requests (e.g., customers, food
...
Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare hav...
For effective matching of resources (e.g., taxis, food, bikes, shopping
...
Cooperative Multi-agent Reinforcement Learning (MARL) is crucial for
coo...
On-demand ride-pooling (e.g., UberPool) has recently become popular beca...
Large-scale screening for potential threats with limited resources and
c...
Real-time traffic signal control systems can effectively reduce urban tr...
In urban environments, supply resources have to be constantly matched to...
With the advent of sequential matching (of supply and demand) systems (u...
Orienteering problems (OPs) are a variant of the well-known prize-collec...