A Competitive Combat Strategy and Tactics in RTS Games AI and StarCraft

Khan, Adil; Yang, Kai-Cheng; Fu, Yunsheng; Lou, Fang; Jifara, Worku; Jiang, Feng; Liu, Shaohui

doi:10.1007/978-3-319-77383-4_1

Cited by 5 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper uses reinforcement learning to explore training agents on the VizDoom testbed. At present, it is considered essential to use reinforcement learning algorithms in three-dimensional environments, for instance, Doom [10], an FPS game presented in FIGURE 1; Warcraft [11,12], a third-person perspective RTS game, and sandbox openworld games like GTA-V [13] and Minecraft [14].…”

Section: Introductionmentioning

confidence: 99%

Using VizDoom Research Platform Scenarios for Benchmarking Reinforcement Learning Algorithms in First-Person Shooter Games

Khan,

Shah,

Khan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Advances in deep reinforcement learning have made it possible to create artificial intelligencebased agents for games that use visual information to make decisions as accurately as humans. Novel procedures are often evaluated in two-dimensional games. However, they are relatively easy compared to three-dimensional games, which have a significantly larger state and action space and, more prominently, contain partially observable states. Thus, this paper trains agents with different reinforcement learning algorithms that work fine in contradiction of human players and in-built agents by evaluating them in the first-person shooter (FPS) game Doom using the VizDoom platform. The agents learned in three different scenarios (maps): ' Defend the Center,' 'Deadly Corridor,' and 'Health gathering.' C51-DDQN, DFP, and REINFORCE algorithms have been proven effective in this study. To assess how well the trained agents performed using various reinforcement learning algorithms, we compared the results of our research with other findings in the literature. Finally, this paper presents a comparative analysis and future research directions.

show abstract

Section: Introductionmentioning

confidence: 99%