Learning to Move Like Professional Counter-Strike Players

• URL: http://arxiv.org/abs/2408.13934v1
• Date: Sun, 25 Aug 2024 20:43:34 GMT
• Title: Learning to Move Like Professional Counter-Strike Players
• Authors: David Durst, Feng Xie, Vishnu Sarukkai, Brennan Shacklett, Iuri Frosio, Chen Tessler, Joohwan Kim, Carly Taylor, Gilbert Bernstein, Sanjiban Choudhury, Pat Hanrahan, Kayvon Fatahalian,
• Abstract summary: We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We train a transformer-based movement model that generates human-like team movement for all players in a "Retakes" round of the game. We show that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.
• Score: 22.974835711827293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a "Retakes" round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of "human-like"). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.

Related papers

• DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision [26.9579556496875]
  Joint manipulation of moving objects and locomotion with legs, such as playing soccer, receive scant attention in the learning community. We propose a feedback control block to compute the necessary body-level movement accurately and using the outputs as dynamic joint-level locomotion supervision. We observe that our learning scheme can not only make the policy network converge faster but also enable soccer robots to perform sophisticated maneuvers.
  arXiv  Detail & Related papers  (2024-03-21T11:16:28Z)
• Physics-based Motion Retargeting from Sparse Inputs [73.94570049637717]
  Commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available.
  arXiv  Detail & Related papers  (2023-07-04T21:57:05Z)
• Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning [26.13655448415553]
  Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. The resulting agent exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more.
  arXiv  Detail & Related papers  (2023-04-26T16:25:54Z)
• Zero-Shot Robot Manipulation from Passive Human Videos [59.193076151832145]
  We develop a framework for extracting agent-agnostic action representations from human videos. Our framework is based on predicting plausible human hand trajectories. We deploy the trained model zero-shot for physical robot manipulation tasks.
  arXiv  Detail & Related papers  (2023-02-03T21:39:52Z)
• GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
  We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots. We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
  arXiv  Detail & Related papers  (2022-09-12T15:14:32Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Action Recognition using Transfer Learning and Majority Voting for CSGO [0.6875312133832078]
  This manuscript aims to develop a model for accurate prediction of 4 different actions and compare the performance among the five different transfer learning models with our self-developed deep neural network. The result of this model aids to the construction of the automated system of gathering and processing more data alongside solving the issue of collecting data from HLTV.
  arXiv  Detail & Related papers  (2021-11-06T13:33:20Z)
• From Motor Control to Team Play in Simulated Humanoid Football [56.86144022071756]
  We train teams of physically simulated humanoid avatars to play football in a realistic virtual environment. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements. They then acquire mid-level football skills such as dribbling and shooting. Finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds.
  arXiv  Detail & Related papers  (2021-05-25T20:17:10Z)
• Counter-Strike Deathmatch with Large-Scale Behavioural Cloning [34.22811814104069]
  This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game Counter-Strike; Global Offensive' from pixel input. The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike play style.
  arXiv  Detail & Related papers  (2021-04-09T09:12:12Z)
• Valuing Player Actions in Counter-Strike: Global Offensive [4.621805808537653]
  Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
  arXiv  Detail & Related papers  (2020-11-02T21:11:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.