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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Combo-Action: Training Agent For FPS Game with Auxiliary Tasks
Authors: Shiyu Huang, Hang Su, Jun Zhu, Ting Chen954-961
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method is efficient in training process and outperforms previous state-of-the-art approaches by a large margin. Ablation study experiments also indicate that our method can boost the performance of the FPS agent in a reasonable way. |
| Researcher Affiliation | Academia | Shiyu Huang, Hang Su, Jun Zhu, Ting Chen Dept. of Comp. Sci. & Tech., BNRist Center, State Key Lab for Intell. Tech. & Sys., Institute for AI, THBI Lab, Tsinghua University, Beijing, 100084, China EMAIL; EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper states 'We collect a set of labeled images for training and testing from the Vi ZDoom environment.' and 'In this paper, we collected 10,000 images for training and use extra 2000 images as validation dataset.' but does not provide access information (link, DOI, repository, or citation) for this dataset. |
| Dataset Splits | Yes | We then split our dataset into 3 partitions: Train: Validate :Test, with ratios 70%:20%:10%. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms and models but does not provide specific ancillary software details like library names with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Our learning rate schedule is as follows: For the ο¬rst 195, 000 steps we start at a high learning rate 10β3. Then we continue training with 10β4 for 3, 000 steps, and ο¬nally 10β5 for 2, 000 steps. Dropout (Srivastava et al. 2014) with ratio 0.5 is used during training. |