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..
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
Authors: Sachin G Konan, Esmaeil Seraj, Matthew Gombolay
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments validate the utility of Info PG by achieving higher sample efficiency and significantly larger cumulative reward in several complex cooperative multi-agent domains. |
| Researcher Affiliation | Academia | Sachin Konan , Esmaeil Seraj , Matthew Gombolay Georgia Institute of Technology Atlanta, GA 30332, USA EMAIL, EMAIL |
| Pseudocode | Yes | Please refer to Appendix, Section A.1 for pseudocode and details of our training and execution procedures. [...] Algorithm 1: Training the Mutual Information Maximizing Policy Gradient (Info PG). |
| Open Source Code | Yes | We also publicized our source code in a public repository, available online at https://github.com/CORE-Robotics-Lab/Info PG. |
| Open Datasets | Yes | Our testing environments include: (1) Cooperative Pong (Co-op Pong) (Terry et al., 2020), (2) Pistonball (Terry et al., 2020), (3) Multiwalker (Gupta et al., 2017; Terry et al., 2020) and, (4) Star Craft II (Vinyals et al., 2017), i.e., the 3M (three marines vs. three marines) challenge. [...] Domains are parts of the Petting Zoo (Terry et al., 2020) MARL research library and can be accessed online at https://www.pettingzoo.ml/envs. The Star Craft II (Vinyals et al., 2017), can be accessed from Deepmind s repository available online at https://github.com/deepmind/pysc2. |
| Dataset Splits | No | The paper discusses training and testing, but does not explicitly mention using a separate validation set or specific training/validation/test splits with percentages or counts. |
| Hardware Specification | Yes | Hardware Specifics All experiments were conducted on an NVIDIA Quadro RTX 8000 with approximately 50 GB of Video Memory Capacity. |
| Software Dependencies | No | The paper discusses the use of Alex Net and specific RNN types (GRU, LSTM, VRNN) but does not provide version numbers for any software dependencies. |
| Experiment Setup | Yes | Additionally, we have provided the details of our implementations for training and execution as well as the full hyperparameter lists for all methods, baselines, and experiments in the Appendix, Section A.9. [...] Tables 2-6 provide detailed hyperparameters such as Learning Rate, Batch Size, Discount Factor, etc. |