This Open-AI Gym compliant environment simulates a OFDM resource allocation task, where a limited number of frequency resources are to be allocated to a large number of User Equipments (UEs) over time. An agent interacting with this Reinforcement Learning (RL) environment plays the role of the MAC scheduler. On each time step, the agent must allocate one frequency resource to one of a large number of UEs. The agent gets rewarded for these resource allocation decisions. The reward increases with the number of UEs, whose traffic requirements are satisfied. The traffic requirements for each UE are expressed in terms of their Guaranteed Bit Rate (if any) and their Packet Delay Budget (PDP).
You are invited to develop a new agent that interacts with this environment and takes effective resource allocation decisions. Such agent must use a Reinforcement Learning algorithm and improve its performance through interaction with the environment. Four sample agents are provided for reference in the comms_rl/agents folder: -Random agent -Round Robin agent -Round Robin IfTraffic agent -Proportional Fair agent
The script scripts/launch_agent.py runs 16 episodes with a maximum of 65536 time steps each, and collects the reward obtained by the agent on each time step. The result is calculated as the average reward obtained in all time steps on all episodes. The performance obtained by the default agents on the default environment configuration is:
Random -69590 Round Robin -69638 Round Robin IfTraffic -3284 Proportional Fair -9595
Note that the above average rewards are negative values. The best performing agent is thus the Round Robin IfTraffic.
The script scripts/launch_agent.py is used to run the interaction between the agent and the environment for a customizable number of episodes. The code has been tested to work on Python 3.7 under Windows 10.