Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
Authors: Yizhe Huang, Anji Liu, Fanqi Kong, Yaodong Yang, Song-Chun Zhu, Xue Feng
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that in mixed-motive environments, HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios. Furthermore, the emergence of social intelligence during our experiments underscores the potential of our approach in complex multi-agent environments. |
| Researcher Affiliation | Academia | 1Institute for Artificial Intelligence, Peking University 2State Key Laboratory of General Artificial Intelligence, BIGAI 3University of California, Los Angeles 4Tsinghua University 5PKU-WUHAN Institute for Artificial Intelligence. |
| Pseudocode | Yes | A. Pseudo Code of HOP (Algorithm 1 HOP) |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes custom simulated environments (Markov Stag-Hunt and Markov Snowdrift Game) rather than using a publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes experiments in simulated environments but does not specify training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies (e.g., libraries, frameworks, or solvers) with their version numbers. |
| Experiment Setup | Yes | Appendix E.3. Hyperparameters (Table 3. Hyperparameters) |