mlsanityData to 7 Oct 2026

PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion

arXiv 2306.04090 ↗First posted 7 Jun 2023cs.AI cs.MAcited by 3

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

Dynamically planning in complex systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing context-dependent decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes make it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we formulate the sequential decision-making process as a conditional trajectory generation process. Based on the formulation, we introduce PlayBest (PLAYer BEhavior SynThesis), a method to improve player decision-making. We extend the diffusion probabilistic model to learn challenging environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained with corresponding rewards. To accomplish reward-guided trajectory generation, we condition the diffusion model on the value function via classifier-guided sampling. We validate the effectiveness of PlayBest through simulation studies, contrasting the generated trajectories with those employed by professional basketball teams. Our results reveal that the model excels at generating reasonable basketball trajectories that produce efficient plays. Moreover, the synthesized play strategies exhibit an alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 23 of its 46 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

23 of 46 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Is Conditional Generative Modeling all you need for Decision-Making?28 Nov

Other references · 22

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  12. 2019Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialogcited by 283
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  2. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  3. 2020Game Plan: What AI can do for Football, and What Football can do for AIcited by 27
  4. 2021ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badmintoncited by 21
  5. 2019DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learningcited by 13
  6. 2020Cracking the Black Box: Distilling Deep Sports Analyticscited by 12
  7. 2022Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badmintoncited by 10
  8. 2019A Bayesian Approach to In-Game Win Probability in Soccercited by 9
  9. 2018The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBAcited by 7
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