Count the whole cost and return a usable result

Include generating and decoding candidates, trials and resets, learning within a trial, optimizer updates, repeated assessment, retained alternatives, communication and memory. EvoJAX explains acceleration of the connected optimizer–policy–task computation; speeding neural inference alone can leave the simulator as the limiting contribution. Deep GA reconstructs parameters from initialization and mutation seeds, reducing stored or transmitted material while adding reconstruction work. Both are conditional implementation choices, not substitutes for informative trials or a suitable representation.

Return the result the next use needs: a tested controller and its interpretation; a learning way with its reset and inheritance rules; executable retained alternatives; or a supported mechanistic conclusion with its remaining ambiguity. Include the condition that would change the next action. If the only missing contribution is an environment adapter, request that adapter with its observation, action and episode-ending behavior. If it is an unexplained specialist operation, return to the linked source or obtain that contribution. Stop with that identified need when it cannot be supplied. The recipient should not have to reconstruct the connection from pattern names or infer a performed experiment from an article describing one.