Papers with Code 아카이브

FPS Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 0
보존된 평가 기록: 0

First-person shooter (FPS) games Involve like call of duty so enjoy

<span style="color:grey; opacity: 0.6">( Image credit: Procedural Urban Environments for FPS Games )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Ardavans/DSR

공식 코드

Deep Successor Reinforcement Learning

https://github.com/TeaPearce/Counter-Strike_Behavioural_Cloning

공식 코드

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

https://github.com/Usama1002/EBE-Exploration

공식 코드

Learning-Driven Exploration for Reinforcement Learning

https://github.com/alex-petrenko/sample-factory

공식 코드

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/glample/Arnold

공식 코드

Playing FPS Games with Deep Reinforcement Learning

https://github.com/mwydmuch/ViZDoom

공식 코드

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/nrasajski/behave

공식 코드

BehAVE: Behaviour Alignment of Video Game Encodings

https://github.com/smerdov/CS_GO_Sensors_Data_Analysis

공식 코드

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

https://github.com/smerdov/smart_chair_client

공식 코드

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

https://github.com/DSobscure/cgi_drl_platform

Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games

https://github.com/KyleChen400/patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/NervanaSystems/coach

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/RENHANFEI/patch_sup

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_health_gathering

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_supreme

Playing FPS Games with Deep Reinforcement Learning

https://github.com/apollopower/DOOM-AI

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/chengyu2/vizdoom_rl_community_canberra

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/devendrachaplot/Neural-Localization

Active Neural Localization

https://github.com/farama-foundation/vizdoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/hegde95/Agents_that_Listen

Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

https://github.com/hegde95/ViZDoom_with_Sound

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/icmlanon58443043/vizdoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/icmlanon58443043/vizdoomicmlanon

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/jackal-u/CSGOAPI

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

https://github.com/jeffchy/Artificial-Idiot

Playing FPS Games with Deep Reinforcement Learning

https://github.com/ngoodger/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/nolanwinsman/Team-Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/opendilab/DI-engine

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/pervasive-ai-lab/nle-language-wrapper

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/sagpant/ViZDoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.