Towards Efficient Detection and Optimal Response against Sophisticated Opponents

Authors: Tianpei Yang, Jianye Hao, Zhaopeng Meng, Chongjie Zhang, Yan Zheng, Ze Zheng

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimental results show both Bayes-To Mo P and deep Bayes-To Mo P outperform the state-of-the-art approaches when faced with different types of opponents in two-agent competitive games.
Researcher Affiliation Collaboration Tianpei Yang1 , Jianye Hao1 , Zhaopeng Meng1 , Chongjie Zhang2 , Yan Zheng1 and Ze Zheng3 1College of Intelligence and Computing, Tianjin University 2MMW, Tsinghua University 3Beifang Investigation, Design & Research CO.LTD
Pseudocode Yes Algorithm 1 Bayes-To Mo P1 algorithm
Open Source Code No The paper references an 'extended version' via an arXiv link (http://arxiv.org/abs/1809.04240) but does not provide an explicit statement about the release of source code for the described methodology or a direct code repository link.
Open Datasets Yes We evaluate the performance of Bayes-To Mo P on the following testbeds: soccer [Littman, 1994; He and Boyd-Graber, 2016] and thieves and hunters [Goodrich et al., 2003; Crandall, 2012].
Dataset Splits No The paper describes using game environments (soccer, thieves and hunters) and training policies, but it does not provide specific dataset split information (e.g., percentages, sample counts, or explicit mention of training, validation, and test sets/episodes) to reproduce data partitioning.
Hardware Specification No No specific hardware details (e.g., CPU/GPU models, memory, or processor types) used for running experiments are provided in the paper.
Software Dependencies No The paper mentions using Q-learning and DQN, but it does not specify any software dependencies with version numbers (e.g., PyTorch version, TensorFlow version, or Python version).
Experiment Setup No The network structure and details of parameter settings are in the extended version1.