A novel real-time design for fighting game AI

Lam, Gia Thuan; Logofătu, Doina; Bǎdicǎ, Costin

doi:10.1007/s12530-020-09351-4

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…[10,11] Nevertheless, action sports are amendable to experimentation with machine learning approaches because highly realistic simulations exist for this domain. [12][13][14] Moreover, naturalistic movements of simulated characters have been studied in the computergraphics community since the early days of animation, and enabling machines to produce animal-type agile maneuvers has been a goal of robotics research. [15][16][17][18] The processes that influence the design of intelligent embodied systems are still not well understood, although it has been acknowledged that these systems also require the integration of multiple control levels.…”

mentioning

confidence: 99%

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

Go,

Jiang,

Loke

2023

Advanced Intelligent Systems

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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.

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