Denoising Opponents Position in Partial Observation Environment

• URL: http://arxiv.org/abs/2310.14553v1
• Date: Mon, 23 Oct 2023 04:16:52 GMT
• Title: Denoising Opponents Position in Partial Observation Environment
• Authors: Aref Sayareh, Aria Sardari, Vahid Khoddami, Nader Zare, Vinicius Prado da Fonseca, Amilcar Soares
• Abstract summary: Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach for each team, competing against each other. We will explain our position prediction idea powered by Long Short-Term Memory models (LSTM) and Deep Neural Networks (DNN)
• Score: 0.4660328753262075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach for each team, competing against each other. The players can only communicate with the Soccer Simulation Server during the game. Several code bases are released publicly to simplify team development. So researchers can easily focus on decision-making and implementing machine learning methods. SS2D actions and behaviors are only partially accurate due to different challenges, such as noise and partial observation. Therefore, one strategy is to implement alternative denoising methods to tackle observation inaccuracy. Our idea is to predict opponent positions while they have yet to be seen in a finite number of cycles using machine learning methods to make more accurate actions such as pass. We will explain our position prediction idea powered by Long Short-Term Memory models (LSTM) and Deep Neural Networks (DNN). The results show that the LSTM and DNN predict the opponents' position more accurately than the standard algorithm, such as the last-seen method.

Related papers

• Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games [0.0]
  Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions. We have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF) We evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies.
  arXiv  Detail & Related papers  (2024-01-07T08:01:25Z)
• Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023 [7.658318240235567]
  This paper presents the latest research of the CYRUS soccer simulation 2D team, the champion of RoboCup 2021. We will explain our denoising idea powered by long short-term memory networks (LSTM) and deep neural networks (DNN)
  arXiv  Detail & Related papers  (2023-05-27T20:46:33Z)
• CYRUS Soccer Simulation 2D Team Description Paper 2022 [8.86121279277966]
  This paper introduces the previous and current research of the CYRUS soccer simulation team. We will present our idea about improving Unmarking Decisioning and Positioning by using Pass Prediction Deep Neural Network.
  arXiv  Detail & Related papers  (2022-05-22T23:16:37Z)
• SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos [62.686484228479095]
  We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each. The dataset is fully annotated with bounding boxes and tracklet IDs. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
  arXiv  Detail & Related papers  (2022-04-14T12:22:12Z)
• 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)
• Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games [31.97631243571394]
  We introduce a framework, LMAC, that automates the discovery of the update rule without explicit human design. Surprisingly, even without human design, the discovered MARL algorithms achieve competitive or even better performance. We show that LMAC is able to generalise from small games to large games, for example training on Kuhn Poker and outperforming PSRO.
  arXiv  Detail & Related papers  (2021-06-04T22:30:25Z)
• 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)
• L2E: Learning to Exploit Your Opponent [66.66334543946672]
  We propose a novel Learning to Exploit framework for implicit opponent modeling. L2E acquires the ability to exploit opponents by a few interactions with different opponents during training. We propose a novel opponent strategy generation algorithm that produces effective opponents for training automatically.
  arXiv  Detail & Related papers  (2021-02-18T14:27:59Z)
• Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning [0.0]
  We will introduce a two-part solution: an open-source Player Tracking model and a new approach to evaluate these players based solely on Deep Reinforcement Learning. Our tracking model was trained in a supervised fashion on datasets we will also release, and our Evaluation Model relies only on simulations of virtual soccer games. We term our new approach Expected Discounted Goal (EDG) as it represents the number of goals a team can score or concede from a particular state.
  arXiv  Detail & Related papers  (2021-01-13T23:26:17Z)
• Learning to Play Sequential Games versus Unknown Opponents [93.8672371143881]
  We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents. Our results include algorithm's regret guarantees that depend on the regularity of the opponent's response.
  arXiv  Detail & Related papers  (2020-07-10T09:33:05Z)
• Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions [103.3630903577951]
  We use cooperative game theory to study teams of artificial RL agents as well as real world teams from professional sports. We introduce a parametric model called cooperative game abstractions (CGAs) for estimating CFs from data. We provide identification results and sample bounds complexity for CGA models as well as error bounds in the estimation of the Shapley Value using CGAs.
  arXiv  Detail & Related papers  (2020-06-16T22:03:36Z)

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.