Universal approximation of reinforcement learning solutions with hypernetworks

An apparatus comprising:

• one or more processors;
• and
• one or more memories, the one or more memories storing instructions configured to cause the apparatus to at least:
• initialize a hypernetwork;
• sample a mini-batch of system parameter sets from a plurality of system parameter sets;
• generate, using the hypernetwork, policy weights for a policy;
• generate, using the hypernetwork, value function weights for a value function;
• calculate a first loss, L_pred, using the mini-batch;
• calculate a second loss, L_TD, using the mini-batch;
• update the hypernetwork using the first loss and the second loss;
• and
• repeatedly perform the sample through update operations until the hypernetwork has converged.

A non-transitory computer readable medium storing instructions, the instructions configured to cause an apparatus to at least:

• initialize a hypernetwork;
• sample a mini-batch of system parameter sets from a plurality of system parameter sets;
• generate, using the hypernetwork, policy weights for a policy;
• generate, using the hypernetwork, value function weights for a value function;
• calculate a first loss, L_pred, using the mini-batch;
• calculate a second loss, L_TD, using the mini-batch;
• update the hypernetwork using the first loss and the second loss;
• and
• repeatedly perform the sample through update operations until the hypernetwork has converged.