mlsanityData to 7 Oct 2026

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

arXiv 2104.11980 ↗First posted 24 Apr 2021cs.LG cs.MAcited by 12

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Abstract (arXiv)

In many multi-agent spatiotemporal systems, agents operate 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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 27 references that are in the map. First posted on arXiv 24 Apr 2021.

Builds on

24 of 27 references.

References · 24

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12 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 4; 2025: 2; 2026: 2.

12 papers.