Abstract: The Reinforcement Learning Based Machine Retirement Planning and Management Apparatuses, Processes and Systems (“MRLAPM”) transforms machine learning training input, order optimization input, withdrawal policy optimization input datastructure/inputs via MRLAPM components into machine learning training output, order optimization output, withdrawal policy optimization output outputs. A machine learning training request datastructure structured to specify an optimal policy reward function and a set of training sample configuration datastructures is obtained. A set of training sample datastructures is generated using the optimal policy reward function and a specified training sample configuration datastructure. An optimal policy is determined using a reinforcement learning technique and the generated set of training sample datastructures. An optimal policy datastructure structured to specify parameters that define the structure of the optimal policy is stored.

Abstract: A method of constructing a portfolio includes receiving target allocations for different types of assets, receiving a list of investments available for inclusion in the portfolio, and selecting investments from the list of investments based on a measure of the risk-adjusted excess return of selected investments and the target allocations.