Model-Free IRL Using Maximum Likelihood Estimation
Authors: Vinamra Jain, Prashant Doshi, Bikramjit Banerjee3951-3958
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With four real-world population data sets in Japan and China, we demonstrate that the proposed method can estimate the transition population more accurately than existing methods. |
| Researcher Affiliation | Industry | Tomoharu Iwata, Hitoshi Shimizu NTT Communication Science Laboratories Kyoto, Japan |
| Pseudocode | Yes | Algorithm 1 shows the estimation procedure of the proposed model. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is open-source or publicly available. |
| Open Datasets | Yes | We evaluated the proposed method using four real-world population data sets: Tokyo, Osaka, Nagoya, and Beijing (Iwata et al. 2017). The Tokyo, Osaka, and Nagoya data... We used the following sources: SNS-based People Flow Data, Nightley, Inc., Shibasaki & Sekimoto Laboratory, the University of Tokyo, Micro Geo Data Forum, People Flow project, and Center for Spatial Information Science at the University of Tokyo, http: //nightley.jp/archives/1954. The Beijing data...were generated from TDrive trajectory data (Yuan et al. 2010; 2011). |
| Dataset Splits | No | The paper describes the datasets used and mentions 'prediction error of the area population at the next time step' for hyperparameter tuning, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'ADAM (Kingma and Ba 2014)' for optimization, but it does not provide specific version numbers for any software dependencies or libraries (e.g., Python, TensorFlow, PyTorch). |
| Experiment Setup | Yes | With the proposed method, we used ten hidden units and maximized the objective function by ADAM (Kingma and Ba 2014). We use the following input vector: utℓℓ = [ τ(t), xℓ, xℓ xℓ]. We transform the input vector into a transition probability by the following three-layered, feed-forward neural network: htℓℓ = tanh(W1utℓℓ + b1), θtℓℓ = softmax(w2htℓℓ + b2). |