Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Authors: Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine. |
| Researcher Affiliation | Collaboration | Chen Sun Google Research Per Karlsson Google Research Jiajun Wu MIT CSAIL Joshua B Tenenbaum MIT CSAIL Kevin Murphy Google Research |
| Pseudocode | No | The paper does not contain any section explicitly labeled 'Pseudocode' or 'Algorithm', nor are there structured steps formatted like an algorithm block. |
| Open Source Code | No | We plan to release the videos along with the game engine after publication of the paper. Video samples can be found at bit.ly/2E3qg6F |
| Open Datasets | Yes | We use the basketball data from Zhan et al. (2018). |
| Dataset Splits | Yes | The hyper parameters, such as the base learning rate and the KL divergence weight β, are tuned on a hold-out validation set. |
| Hardware Specification | Yes | The models are trained on 6 V100 GPUs with synchronous training with batch size of 8 per GPU, we train the model for 80K steps on soccer and 40K steps on basketball. |
| Software Dependencies | No | The paper mentions software components such as Res Net-18, S3D, GRUs, MLPs, relation networks, and the Unity game engine, but it does not provide specific version numbers for any of these components or other libraries. |
| Experiment Setup | Yes | Our visual encoder is based on Res Net-18 (He et al., 2016), we use the first two blocks of Res Net to maintain spatial resolution, and then aggregate the feature map with max pooling. The encoder is pre-trained on visible players, and then fine-tuned for each baseline. For the soccer data, we down-sample the video to 4 FPS, and treat 4 frames (1 second) as one step. We consider 10 steps in total, 6 observed, 4 unobserved. We set the size of GRU hidden states to 128 for all baselines. The state decoder is a 2-layer MLP. For basketball data, we set every 5 frames as one step, and consider 10 steps as well. The size of GRU hidden states is set to 128. The models are trained on 6 V100 GPUs with synchronous training with batch size of 8 per GPU, we train the model for 80K steps on soccer and 40K steps on basketball. We use a linear learning rate warmup schedule for the first 1K steps, followed by a cosine learning rate schedule. The hyper parameters, such as the base learning rate and the KL divergence weight β, are tuned on a hold-out validation set. |