A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

Abstract

The interactions with humans, and simultaneously, making of real‐time decisions in physical systems, are involved in many applications of artificial intelligence. An example of these conditions is maneuver sports. Movement‐type simulations, viz., the esports game Street Fighter (SF), recapitulate the complex multicharacter interactions and, concurrently, generate the millisecond‐level control challenges of human athletes. Herein, the physical and mental signatures of the SF agent (it is called SF R2) are controlled by utilizing a previously unreported model‐free, natural, deep reinforcement learning algorithm “Decay‐based Phase‐change memristive character‐type Proximal Policy Optimization” (DP‐PPO) through an assemblage of hybrid case‐type training processes; and an integrated training configuration for time‐trial evaluations, as well as competitions with a world's best SF player, is developed. A short length of time utilized by the SF R2 to defeat the opponent and, simultaneously, maintaining a good health level is achieved, as well as excellent handling of imperfect information settings. Training studies reveal a moderate maneuver etiquette in the SF R2, along with rapid, effective head‐to‐head competitions with one of the world's best SF player. This paves the way for achieving a broadly applicable training scheme, capable of quickly controlling complicated‐movement systems in fields where agents should observe unspecified human norms.