most citedGym-RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

2 citations · 2 across the 1 of their papers we have counts for

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1 paper

cs.LG2021★ 2 cited

Gym-RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

Shengyi Huang, Santiago Ontañón, Chris Bamford +1

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agen…