Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Authors: Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method in huge 2D gridworlds and a variety of benchmark environments, including Atari 2600 and Mu Jo Co. Experimental results validate that our method outperforms baseline approaches in most tasks in terms of mean scores and exploration efficiency. |
| Researcher Affiliation | Academia | Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, and Chun-Yi Lee Department of Computer Science, National Tsing Hua University |
| Pseudocode | No | The paper describes the proposed algorithms (Div-DQN, Div-DDPG, Div-A2C) using mathematical equations and textual explanations, but does not provide any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper mentions '1https://github.com/openai/baselines' as the source for their baseline implementations, but does not provide any explicit statement or link for the open-source code of their proposed methodology. |
| Open Datasets | Yes | Atari 2600. For discrete control tasks, we perform experiments in the Arcade Learning Environment (ALE) [20]... Mu Jo Co. For continuous control tasks, we conduct experiments in environments built on the Mu Jo Co physics engine [4]. |
| Dataset Splits | No | The paper mentions training with 40M frames for Atari games and evaluation of 'in-training median scores', but it does not provide specific percentages or absolute counts for training, validation, or test dataset splits, nor does it refer to standard predefined splits for these environments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models (e.g., NVIDIA A100, Tesla V100), CPU models, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper states that 'All of these baselines are implemented based on Open AI Baselines', but it does not specify version numbers for any software dependencies, libraries, or frameworks used in the implementation or experimentation. |
| Experiment Setup | No | The paper states 'Our hyperparameter settings are provided in the supplementary material.', indicating that specific experimental setup details are not present in the main text. |