Randomized Adversarial Imitation Learning for Autonomous Driving
Authors: MyungJae Shin, Joongheon Kim
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the performance between RAIL and baselines. Furthermore, in order to assess the performance gaps between the single-layer and multi-layer policies trained by RAIL, the single-layer and two-layer (i.e., multi-layer) policies was implemented. |
| Researcher Affiliation | Academia | Myung Jae Shin , Joongheon Kim Chung-Ang University, Seoul, Republic of Korea {mjshin.cau, joongheon}@gmail.com |
| Pseudocode | Yes | Algorithm 1: RAIL |
| Open Source Code | No | The paper does not provide any explicit statement or link to its open-source code. |
| Open Datasets | No | We make the expert demonstration using PPO with specific action controls. The results present the average of 16 experiment results. In the experiments, the trained weights by BC are used to fast convergence in GAIL and RAIL. |
| Dataset Splits | No | The paper mentions average episodes and trajectories but does not specify explicit train/validation/test dataset splits or percentages. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running experiments. |
| Software Dependencies | No | The paper states 'We implemented the RAIL simulator based on Unity' but does not specify version numbers for Unity or other software dependencies. |
| Experiment Setup | Yes | Hyperparameters: α step size, N number of sampled directions per iteration, δi and δo Gaussian vectors from zero mean and ν a positive real number standard deviation of the exploration noise, h hidden layer size Initialize : θi, θo from behavior cloning, µ0 = 0 Rn, and P0 = In Rn n |