Reinforcement learning model training through simulation

A first system, comprising:

• one or more processors;
• and
• memory that stores computer-executable instructions that, if executed, cause the first system to:
• obtain first computer-executable code defining a reinforcement function for training a reinforcement learning model for a second system;
• evaluate the first computer-implemented executable code to identify one or more suggestions to modify the first computer-implemented executable code;
• modify the first computer-implemented executable code based at least on the one or more suggestions to generate a second computer-implemented executable code defining the custom-designed reinforcement function, the one or more suggestions determined based at least in part on historical data associated with generation of computer-executable code for one or more other reinforcement functions;
• receive a request to perform reinforcement learning for the second system;
• configure a simulation environment by injecting the second computer-executable code into a simulation application for the second system;
• and
• perform the reinforcement learning using the simulation environment.

A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:

• obtain first computer-executable code defining a custom-designed function for training a model for a second system;
• evaluate the first computer-implemented executable code to identify one or more suggestions to modify the first computer-implemented executable code;
• modify the first computer-implemented executable code based at least on the one or more suggestions to generate a second computer-implemented executable code defining the custom-designed function, the one or more suggestions determined based at least in part on data associated with generation of prior computer-executable code for one or more other custom-designed functions;
• configure, in response to a request to perform reinforcement learning for the second system, a simulation environment by injecting the second computer-executable code into a simulation application;
• and
• perform, using the simulation environment, the reinforcement learning.