| CPC G06N 3/08 (2013.01) [G06F 30/20 (2020.01)] | 19 Claims |
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1. A system for generating trained reinforcement learning models, the system comprising:
a plurality of processing devices corresponding to a simulation environment for a set of simulated, hosted customer networks; and
a non-transitory computer readable medium storing instructions that, when executed by the at least a first processing device, cause the system to perform operations including:
receiving, from a client device, a request to provide a generated trained reinforcement learning model on training data generated from an identified first simulated, hosted customer network;
responsive to the request from the client device, selecting a first simulated, hosted customer network from a set of simulated, hosted customer networks using the received request as selection criteria;
identifying, from a data store, a simulator for generating the training data corresponding to a network simulation of an identified network and for training a reinforcement learning model based at least in part on the request;
identifying a reference reinforcement learning model for the request;
instantiating a simulation environment based at least in part on a parameter from an environment used to train the reference reinforcement learning model;
instantiating an agent in the simulation environment including the simulator, the agent being configured using at least one parameter used to train the reference reinforcement learning model;
activating the agent for a simulation period to form the trained reinforcement learning model, wherein each activation includes a state and a reward for a previous action taken by the agent; and
providing to a client device the generated trained reinforcement learning model, of a simulated reinforcement learning model of the training data, in response to the request.
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