A Survey of Reinforcement Learning Toolkits for Gaming: Applications, Challenges and Trends

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

“…[45,46] The library supported reinforcement learning studies for developing next-generation computer games and was compatible with various emulators. [41,74] In traditional studies, reinforcement learningbased agents required asynchronous interactions between the computer and the game using an internet protocol, which was susceptible to network delays. [1,3,75] On the other hand, in this work, the gym-retro environment, i.e., the application-programming interface (API), waited for the SF R2 agent to execute actions based on the latest observation of the agent before the game proceeded to the next frame, thus alleviating the problem of communication delays.…”

“…[33][34][35][36] As traditional autonomous competitions limit the challenges to performing fixed poses and simple object identification, [37,38] maneuvers powered by fully unmanned characters could be several years away.To avoid this modeling complexity, researchers have explored various routes to utilize machine learning, such as harnessing evolutionary strategies, imitation learning, and reinforcement learning to learn movement policies, as well as utilizing supervised learning to model character maneuvers. [39][40][41] The action at a high level is not well understood, although traditional studies have demonstrated excellent performance in solo maneuvers or progressed to simple navigation scenarios. [11,[42][43][44] Human athletes are required to be highly skilled in four areas: 1) maneuver tactics, 2) human-body control, 3) movement strategy, and 4) navigation etiquette, to be effective.…”

“…To avoid this modeling complexity, researchers have explored various routes to utilize machine learning, such as harnessing evolutionary strategies, imitation learning, and reinforcement learning to learn movement policies, as well as utilizing supervised learning to model character maneuvers. [39][40][41] The action at a high level is not well understood, although traditional studies have demonstrated excellent performance in solo maneuvers or progressed to simple navigation scenarios. [11,[42][43][44] Human athletes are required to be highly skilled in four areas: 1) maneuver tactics, 2) human-body control, 3) movement strategy, and 4) navigation etiquette, to be effective.…”