Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
Authors: Yuxin Wu, Yuandong Tian
ICLR 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,247 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,466 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we show the training procedure (Sec. 5.1), evaluate our AIs with ablation analysis (Sec. 5.2) and Vi ZDoom AI Competition (Sec. 5.3). |
| Researcher Affiliation | Collaboration | Yuxin Wu Carnegie Mellon University EMAIL Yuandong Tian Facebook AI Research EMAIL |
| Pseudocode | No | The paper describes the model and training procedure in detail, but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper mentions "tensorpack" which has a GitHub link (5https://github.com/ppwwyyxx/tensorpack), but this is a framework used by the authors, not the specific code implementation for *this* paper's methodology. There is no explicit statement or link for the paper's own source code. |
| Open Datasets | No | The paper uses the Vi ZDoom platform and describes custom scenarios like "Flat Map" and "CIGTrack1", but does not provide any public access information (link, citation, repository) for the specific datasets or scenarios used in their experiments. |
| Dataset Splits | No | The paper mentions using "100 episodes" or "300 episodes" for evaluation but does not specify a formal train/validation/test split for a dataset. |
| Hardware Specification | Yes | The training procedure runs on Intel Xeon CPU E5-2680v2 at 2. 80GHz, and 2 Titan X GPUs. |
| Software Dependencies | No | Our training procedure is implemented with Tensor Flow [Abadi et al. (2016)] and tensorpack5. (No version numbers provided for TensorFlow or tensorpack.) |
| Experiment Setup | Yes | We use Adam [Kingma & Ba (2014)] with ǫ = 10 3 for training. Batch size is 128, discount factor γ = 0.99, learning rate α = 10 4 and the policy learning rate β = 0.08α. |