Papers FPS Games
“FPS Games” 태그가 달린 논문 19편 · 필터 해제
Agents Play Thousands of 3D Video Games
We present PORTAL, a novel framework for developing artificial intelligence agents capable of playing thousands of 3D video games through language-guided policy generation. By transforming decision-making problems into l…
FPS GamesLanguage ModelingLanguage ModellingTraining Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning
In the realm of competitive gaming, 3D first-person shooter (FPS) games have gained immense popularity, prompting the development of game AI systems to enhance gameplay. However, deploying game AI in practical scenarios …
Deep Reinforcement LearningFPS GamesIdentify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games
The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can l…
FPS GamesPathfinderBehAVE: Behaviour Alignment of Video Game Encodings
Domain randomisation enhances the transferability of vision models across visually distinct domains with similar content. However, current methods heavily depend on intricate simulation engines, hampering feasibility and…
DiversityFPS GamesVideo UnderstandingAutomatic Bug Detection in Games using LSTM Networks
We introduced a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in video games as anomalies. The detected buggy frames are then clustered to determine the categor…
FPS GamesTowards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games
In this paper, we propose a new approach called Adaptive Behavioral Costs in Reinforcement Learning (ABC-RL) for training a human-like agent with competitive strength. While deep reinforcement learning agents have recent…
Decision MakingDeep Reinforcement LearningFPS Gamesreinforcement-learning+2Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning
This paper offers an overall solution for first-person shooter (FPS) games to achieve superior performance using general reinforcement learning (RL). We introduce an agent in ViZDoom that can surpass previous top agents …
FPS GamesGeneral Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input. The agent, a deep neural network, matches the performance of the m…
AI AgentBehavioural cloningFPS GamesImitation LearningAugmenting Automated Game Testing with Deep Reinforcement Learning
General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement learning (DRL), we introduce a self-lear…
Deep Reinforcement LearningFPS Gamesreinforcement-learningReinforcement Learning+2AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports…
Feature EngineeringFeature ImportanceFPS GamesSensor Modeling+3Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in sim-to-real transfer for robotics. Typical…
FPS GamesGeneral Reinforcement LearningGPUMulti-agent Reinforcement Learning+3Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection…
BIG-bench Machine LearningFeature EngineeringFeature ImportanceFPS Games+4eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair
Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: …
Feature EngineeringFeature ImportanceFPS GamesSensor Modeling+3Learning-Driven Exploration for Reinforcement Learning
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $\epsilon$-greedy exploration or add…
Efficient ExplorationFPS Gamesreinforcement-learningReinforcement Learning+1Active Neural Localization
Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are …
FPS GamesGame of DoomReinforcement LearningPlaying FPS Games with Deep Reinforcement Learning
Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in…
Deep Reinforcement LearningFPS GamesGame of DoomQ-Learning+3Deep Successor Reinforcement Learning
Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (S…
Deep Reinforcement LearningFPS GamesGame of Doomreinforcement-learning+2ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 game…
Atari GamesFPS GamesGame of DoomQ-Learning+3Procedural urban environments for FPS games
This paper presents a novel approach to procedural generation of urban maps for First Person Shooter (FPS) games. A multi-agent evolutionary system is employed to place streets, buildings and other items inside the Unity…
BIG-bench Machine LearningFPS Games