Maximizing Throughput of Aerial Base Stations via Resources-based Multi-Agent Proximal Policy Optimization: A Deep Reinforcement Learning Approach.
Yu Min ParkSheikh Salman HassanChoong Seon HongPublished in: APNOMS (2022)
Keyphrases
- reinforcement learning
- multi agent
- base station
- optimal policy
- markov decision process
- division multiple access
- policy search
- action selection
- wireless networks
- wireless sensor networks
- cellular networks
- mobile networks
- mobile users
- function approximation
- state space
- markov decision processes
- function approximators
- sensor networks
- resource allocation
- multiple agents
- control policy
- response time
- communication networks
- policy gradient
- partially observable markov decision processes
- reward function
- single agent
- learning algorithm
- multi hop
- intelligent agents
- scheduling policies
- learning agents
- model free
- real time