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..
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
Authors: Jingwu Tang, Gokul Swamy, Fei Fang, Steven Z. Wu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theory paper. We do not include experiments for the algorithms. |
| Researcher Affiliation | Academia | Jingwu Tang Carnegie Mellon University EMAIL Gokul Swamy Carnegie Mellon University EMAIL Fei Fang Carnegie Mellon University EMAIL Zhiwei Steven Wu Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 MALICE (Multi-agent Aggregation of Losses to Imitate Cached Experts) |
| Open Source Code | No | This is a theory paper. We do not include experiments for the algorithms. |
| Open Datasets | No | This is a theory paper. We do not include experiments for the algorithms. |
| Dataset Splits | No | This is a theory paper. We do not include experiments for the algorithms. |
| Hardware Specification | No | This is a theory paper. We do not include experiments for the algorithms. |
| Software Dependencies | No | This is a theory paper. We do not include experiments for the algorithms. |
| Experiment Setup | No | This is a theory paper. We do not include experiments for the algorithms. |