Generating Multi-Agent Trajectories using Programmatic Weak Supervision
Authors: Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts. |
| Researcher Affiliation | Collaboration | Eric Zhan Caltech ezhan@caltech.edu Stephan Zheng Salesforce stephan.zheng@salesforce.com Yisong Yue Caltech yyue@caltech.edu Long Sha & Patrick Lucey STATS {lsha,plucey}@stats.com |
| Pseudocode | Yes | Algorithm 1 describes LF-window25, which computes macro-intents based on last positions in 25-timestep windows (LF-window50 is similar). Algorithm 2 describes LF-stationary, which computes macro-intents based on stationary positions. |
| Open Source Code | Yes | Code is available at https://github.com/ezhan94/multiagent-programmatic-supervision. |
| Open Datasets | Yes | Dataset was provided by STATS: https://www.stats.com/data-science/. |
| Dataset Splits | No | The paper states 'There are 107,146 training and 13,845 test examples.' but does not explicitly mention a validation dataset split. |
| Hardware Specification | No | Information not found. The paper does not specify the hardware used for running its experiments. |
| Software Dependencies | No | Information not found. The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | We model each latent variable zk t as a multivariate Gaussian with diagonal covariance of dimension 16. All output models are implemented with memory-less 2-layer fully-connected neural networks with a hidden layer of size 200. Our agent-models sample from a multivariate Gaussian with diagonal covariance while our macro-intent models sample from a multinomial distribution over the macro-intents. All hidden states (hg,t, h1 t, . . . h K t ) are modeled with 200 2-layer GRU memory cells each. We maximize the log-likelihood/ELBO with stochastic gradient descent using the Adam optimizer (Kingma & Ba, 2015) and a learning rate of 0.0001. |