FPS Games
12 papers with code • 0 benchmarks • 0 datasets
First-person shooter (FPS) games Involve like call of duty so enjoy
( Image credit: Procedural Urban Environments for FPS Games )
Benchmarks
These leaderboards are used to track progress in FPS Games
Most implemented papers
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
mwydmuch/ViZDoom
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Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.
Playing FPS Games with Deep Reinforcement Learning
glample/Arnold
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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.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
alex-petrenko/sample-factory
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ICML 2020
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
TeaPearce/Counter-Strike_Behavioural_Cloning
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This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
Deep Successor Reinforcement Learning
The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards.
Active Neural Localization
devendrachaplot/Neural-Localization
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ICLR 2018
The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations.
Learning-Driven Exploration for Reinforcement Learning
We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space.
Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
eSports is the rapidly developing multidisciplinary domain.
eSports 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.
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
smerdov/CS_GO_Sensors_Data_Analysis
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For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players.
