Multi-objective multi-policy reinforcement learning system

Abstract

Systems and methods described herein can involve obtaining Pareto optimal solutions through making sequential decisions in a system that has multi-dimensional rewards and a continuous state space, and is controllable through a finite discrete set of actions, involving learning a value function through reinforcement learning (RL), wherein the value function is configured to take in an input of a state and an action pair, and provides a set of vectors as output, each of the set of vectors representing an expected total sum of rewards corresponding to a sequence of future control decisions; receiving, at an initial stage of a control sequence, a request about a total sum of rewards to be achieved; and determining a sequence of actions iteratively based on the output of the value function, an observation of the current state, and the request.

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