Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning
Authors: Kyungsoo Kim, Jeongsoo Ha, Yusung Kim
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a set of Mu Jo Co visual control tasks and an autonomous driving task (CARLA), SPD outperforms previous studies in complex observations, and significantly improves the generalization performance for unseen observations. Our code is available at https://github.com/unigary/SPD. |
| Researcher Affiliation | Academia | Kyungsoo Kim , Jeongsoo Ha and Yusung Kim Sungkyunkwan University {unigary, hjg1210}@g.skku.edu, yskim525@skku.edu |
| Pseudocode | Yes | Algorithm 1 Self-Predictive Dynamics |
| Open Source Code | Yes | Our code is available at https://github.com/unigary/SPD. |
| Open Datasets | Yes | For evaluation, we used a set of continuous control tasks (the Deep Mind Control suite [Tassa et al., 2018]) with distracting elements backgrounds as proposed in [Zhang et al., 2020]. In an autonomous driving task, CARLA [Dosovitskiy et al., 2017], our method achieves the best performance on complex observations containing a lot of task-irrelevant information in realistic driving scenes. |
| Dataset Splits | No | The paper describes training on different backgrounds (Simple Distractor, Natural Video) and testing for generalization across them. However, it does not specify explicit train/validation/test dataset splits with percentages, sample counts, or predefined partition citations. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processors, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Implementation details and hyper parameters are in the supplementary material.' but does not list specific software dependencies with version numbers in the provided main text. |
| Experiment Setup | No | The paper states 'Implementation details and hyper parameters are in the supplementary material.' but does not provide concrete experimental setup details, such as specific hyperparameter values or training configurations, in the main text. |