Policy-based scheduling of wireless resources

A method for performing for a time unit a scheduling operation comprising:

• receiving a time domain scheduling decision for the time unit, the time domain scheduling decision indicating a set of devices of a wireless communication system;
• determining a feature vector descriptive of the set of devices and an available set of frequency resource units of the wireless communication system;
• inputting the feature vector to a policy-based reinforcement learning agent for receiving an output, the output comprising a distribution between the set of devices and the set of frequency resource units;
• and
• using the distribution to determine a frequency domain scheduling decision.

An apparatus for training a policy-based reinforcement learning agent with an environment defined by a wireless communication system, the apparatus comprising at least one processor;

• and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform a scheduling operation per training time unit of a plurality of training time units that comprises:
• receive a time domain scheduling decision for the training time unit, the time domain scheduling decision indicating a set of devices;
• determine a feature vector descriptive of the set of devices and an available set of frequency resource units of the wireless communication system;
• input the feature vector to the policy-based reinforcement learning agent for receiving an output, the output comprising a distribution between the set of devices and the set of frequency resource units;
• use the distribution to determine a frequency domain scheduling decision;
• and
• check a convergence criterion by at least:
• use a combination of one or more rewards to determine whether the convergence criterion is fulfilled, the combination comprising a current reward associated with the determined frequency domain scheduling decision;
• in response to determining that the convergence criterion is fulfilled provide the policy-based reinforcement learning agent as a trained policy-based reinforcement learning agent;
• otherwise, adapt learnable parameters of the policy-based reinforcement learning agent, resulting in an adapted policy-based reinforcement learning agent and performing the scheduling operation for a next training time unit using the adapted policy-based reinforcement learning agent.