Neural Amortized Inference for Nested Multi-Agent Reasoning
Authors: Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, Tianmin Shu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy. |
| Researcher Affiliation | Collaboration | Kunal Jha1, Tuan Anh Le2, Chuanyang Jin3, Yen-Ling Kuo4, Joshua B. Tenenbaum5, Tianmin Shu5,6 1Dartmouth College 2Google Research 3New York University 4University of Virginia 5Massachusetts Institute of Technology 6Johns Hopkins University |
| Pseudocode | No | The paper describes algorithmic steps in paragraph format but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | 1The code and the supplementary material are available at https: //www.tshu.io/nested reasoning. |
| Open Datasets | No | The paper describes synthesizing its own datasets for experiments ('This allows us to create two training sets S1... and S2...', 'create three training sets: S0...'), and mentions that 'We describe how we generate the training data in the supplementary material.' However, it does not provide concrete access information (like a link or citation to a public repository) for the generated datasets themselves, only the code to generate them. |
| Dataset Splits | No | The paper mentions 'training sets' and 'testing episodes' but does not explicitly provide details about validation dataset splits (e.g., percentages, sample counts, or methods for creating a validation set). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions using 'CARLO (Cao et al. 2020)' for the driving environment but does not specify version numbers for any software, libraries, or frameworks used in their implementation. |
| Experiment Setup | Yes | We evaluate our method in a 2D grid-world domain, Construction... The two agents and ten colored blocks are randomly spawned in 20 20 grid. There are 45 possible goals for Alice and two possible goals for Bob. |