baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents

• URL: http://arxiv.org/abs/2104.11980v1
• Date: Sat, 24 Apr 2021 16:20:47 GMT
• Title: baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
• Authors: Michael A. Alcorn, Anh Nguyen
• Abstract summary: We introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. We show that baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset.
• Score: 17.352818121007576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many multi-agent spatiotemporal systems, the agents are under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent models implicitly assume the agents' trajectories are statistically independent at each time step. In this paper, we introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. Specifically, baller2vec++ applies a specially designed self-attention mask to a mixture of location and "look-ahead" trajectory sequences to learn the distributions of statistically dependent agent trajectories. We show that, unlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset. Additionally, when modeling the trajectories of professional basketball players, baller2vec++ outperforms baller2vec by a wide margin.

Related papers

• TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports [28.32714256545306]
  TranSPORTmer is a unified transformer-based framework capable of addressing all these tasks. It effectively captures temporal dynamics and social interactions in an equivariant manner. It outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation.
  arXiv  Detail & Related papers  (2024-10-23T11:35:44Z)
• xLAM: A Family of Large Action Models to Empower AI Agent Systems [111.5719694445345]
  We release xLAM, a series of large action models designed for AI agent tasks. xLAM consistently delivers exceptional performance across multiple agent ability benchmarks.
  arXiv  Detail & Related papers  (2024-09-05T03:22:22Z)
• Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer encoder for spatial feature extraction. We benchmark three practical sports game datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM [18.884300680050316]
  This paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. We suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics.
  arXiv  Detail & Related papers  (2023-06-14T02:19:59Z)
• MADiff: Offline Multi-agent Learning with Diffusion Models [79.18130544233794]
  Diffusion model (DM) recently achieved huge success in various scenarios including offline reinforcement learning. We propose MADiff, a novel generative multi-agent learning framework to tackle this problem. Our experiments show the superior performance of MADiff compared to baseline algorithms in a wide range of multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:14:09Z)
• AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting [25.151713845738335]
  We propose a new Transformer, AgentFormer, that jointly models the time and social dimensions. Based on AgentFormer, we propose a multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep. Our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
  arXiv  Detail & Related papers  (2021-03-25T17:59:01Z)
• baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling [17.352818121007576]
  Multi-agenttemporal modeling is a challenging task from both an algorithmic design perspective and computational perspective. We introduce baller2vec, a multi-entity generalization of the standard Transformer that can simultaneously integrate information across entities and time. We test the effectiveness of baller2vec for multi-agenttemporal modeling by training it to perform two different basketball-related tasks.
  arXiv  Detail & Related papers  (2021-02-05T17:02:04Z)
• Multi-Agent Collaboration via Reward Attribution Decomposition [75.36911959491228]
  We propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge. CollaQ is evaluated on various StarCraft Attribution maps and shows that it outperforms existing state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-10-16T17:42:11Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation [53.262360083572005]
  We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL) We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL) and game-theoretic RL (GT-RL) Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.
  arXiv  Detail & Related papers  (2020-03-21T00:43:54Z)

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.