Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization

02/24/2023
by   Adriano Mendo, et al.
0

This paper proposes a method for wireless network optimization applicable to tuning cell parameters that impact the performance of the adjusted cell and the surrounding neighboring cells. The method relies on multiple reinforcement learning agents that share a common policy and include information from neighboring cells in the state and reward. In order not to impair network performance during the first steps of learning, agents are pre-trained during an earlier phase of offline learning, in which an initial policy is obtained using feedback from a static network simulator and considering a wide variety of scenarios. Finally, agents can wisely tune the cell parameters of a test network by suggesting small incremental changes to slowly steer the network toward an optimal configuration. Agents propose optimal changes using the experience gained with the simulator in the pre-training phase, but also continue to learn from current network readings after each change. The results show how the proposed approach significantly improves the performance gains already provided by expert system-based methods when applied to remote antenna tilt optimization. Additional gains are also seen when comparing the proposed approach with a similar method in which the state and reward do not include information from neighboring cells.

READ FULL TEXT
research
04/04/2023

Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning

Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a ...
research
07/03/2022

NVIF: Neighboring Variational Information Flow for Large-Scale Cooperative Multi-Agent Scenarios

Communication-based multi-agent reinforcement learning (MARL) provides i...
research
11/24/2020

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning...
research
05/20/2022

Task Relabelling for Multi-task Transfer using Successor Features

Deep Reinforcement Learning has been very successful recently with vario...
research
11/11/2021

Offline Contextual Bandits for Wireless Network Optimization

The explosion in mobile data traffic together with the ever-increasing e...
research
09/10/2021

Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems

A multi-agent deep reinforcement learning (MADRL) is a promising approac...
research
03/02/2023

T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy

T cells monitor the health status of cells by identifying foreign peptid...

Please sign up or login with your details

Forgot password? Click here to reset