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..
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation
Authors: Junyu Liu, Xiangjun Peng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption. |
| Researcher Affiliation | Academia | Junyu Liu Brown University EMAIL Xiangjun Peng The Chinese University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 in Appendix E illustrates the pseudo-code for pre-computing available Feint behavior templates given a set of available attack behaviors B. Algorithm 2 in Appendix E shows the pseudo-code for composing available Dual-Behavior models with backward searches. |
| Open Source Code | No | The NeurIPS checklist states 'No' for open access to data and code, justifying that the contribution is a formalization and implementation is based on existing frameworks, not releasing their own specific implementation code. |
| Open Datasets | Yes | Our main testbed game environment is a multi-player boxing game, which is based on Open AI s open-source environment Multi-Agent Particle Environment [23], but with heavy additional implementation to create a physically realistic scenario. We also modify and extend a strategic real-world game, Alpha Star [3], which is widely used as the experimental testbed in recent studies of Reinforcement Learning studies [28, 19]. |
| Dataset Splits | No | The paper specifies training iterations but does not explicitly mention validation dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions that its implementation is based on 'Johannesack/tf2multiagentrl [1]', but does not specify exact version numbers for programming languages, libraries, or other key software dependencies. |
| Experiment Setup | Yes | All experiments for the two-player scenario are trained for 75,000 game iterations and all experiments for the six-player scenario are trained for 150,000 game iterations. |