Radio access network control with deep reinforcement learning

A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:

• obtaining operational data from a radio access network;
• formatting the operational data into state information and reward information for a reinforcement learning agent;
• processing the state information and the reward information via the reinforcement learning agent, wherein the reinforcement learning agent comprises a plurality of sub-agents, wherein each of the plurality of sub-agents comprises a respective neural network of a plurality of neural networks, wherein each of the plurality of neural networks encodes a respective policy for selecting at least one setting of at least one parameter of the radio access network to increase a respective predicted reward in accordance with the state information, wherein each of the plurality of neural networks is updated in accordance with the reward information;
• determining a plurality of settings for a plurality of parameters of the radio access network via the reinforcement learning agent, wherein the reinforcement learning agent determines the plurality of settings in accordance with a plurality of selections for the plurality of settings via the plurality of sub-agents, wherein the plurality of settings includes the at least one setting and the plurality of parameters includes the at least one parameter;
• and
• applying the plurality of settings to the radio access network.

An apparatus comprising:

• a processing system including at least one processor;
• and
• a computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
• obtaining operational data from a radio access network;
• formatting the operational data into state information and reward information for a reinforcement learning agent;
• processing the state information and the reward information via the reinforcement learning agent, wherein the reinforcement learning agent comprises a plurality of sub-agents, wherein each of the plurality of sub-agents comprises a respective neural network of a plurality of neural networks, wherein each of the plurality of neural networks encodes a respective policy for selecting at least one setting of at least one parameter of the radio access network to increase a respective predicted reward in accordance with the state information, wherein each of the plurality of neural networks is updated in accordance with the reward information;
• determining a plurality of settings for a plurality of parameters of the radio access network via the reinforcement learning agent, wherein the reinforcement learning agent determines the plurality of settings in accordance with a plurality of selections for the plurality of settings via the plurality of sub-agents, wherein the plurality of settings includes the at least one setting and the plurality of parameters includes the at least one parameter;
• and
• applying the plurality of settings to the radio access network.