Task Transfer by Preference-Based Cost Learning

Authors: Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu2471-2478

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

Reproducibility Variable Result LLM Response
Research Type Experimental Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.
Researcher Affiliation Collaboration Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China Tencent AI Lab, Shenzhen, Guangdong, China {jmx16, maxj14}@mails.tsinghua.edu.cn, {fcsun, hpliu}@tsinghua.edu.cn hwenbing@126.com
Pseudocode Yes Algorithm 1 Preference-based task transfer via Adversarial Max Ent IRL
Open Source Code No The paper does not provide a direct link or explicit statement for the open-sourcing of the code for its described methodology.
Open Datasets No The paper mentions environments like Mu Jo Co and Open AI Gym, and states that 'initial demonstrations are generated by a well-trained PPO', but does not provide access information for a publicly available or open dataset.
Dataset Splits No The paper does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing.
Hardware Specification No The paper does not describe the specific hardware (e.g., CPU/GPU models, memory) used to run its experiments.
Software Dependencies No The paper mentions 'Mu Jo Co' and 'Open AI Gym' but does not provide specific version numbers for these or other software dependencies.
Experiment Setup No The paper describes the general iterative process of the algorithm but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, optimizer settings) in the main text.