Revisiting Data Augmentation in Deep Reinforcement Learning
Authors: Jianshu Hu, Yunpeng Jiang, Paul Weng
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 EXPERIMENTAL RESULTS In order to validate our theoretical analysis and show the effectiveness of our proposed algorithm, we perform a series of experiments to (1) experimentally validate our propositions, (2) conduct a case study explicitly showing the statistics we analyzed, (3) compare our final proposed algorithm with state-of-the-art baselines (RAD, Dr AC, Dr Q, Dr Qv2, SVEA) to verify its sample efficiency, and evaluate its generalization ability against SVEA, which was specifically-designed for this purpose. |
| Researcher Affiliation | Academia | Jianshu Hu, Yunpeng Jiang UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai, China {hjs1998,jyp9961}@sjtu.edu.cn Paul Weng Data Science Research Center Duke Kunshan University Kunshan, Jiangsu, China paul.weng@duke.edu |
| Pseudocode | Yes | Algorithm 1 Data-Augmented Off-policy Actor-Critic Scheme |
| Open Source Code | Yes | 1The source code of our method: https://github.com/Jianshu-Hu/drqv2 |
| Open Datasets | Yes | We evaluate different methods on environments from Deep Mind Control Suite (Tassa et al., 2018) |
| Dataset Splits | No | The paper evaluates different methods on Deep Mind Control Suite environments, but does not specify explicit training/validation/test dataset splits with percentages, counts, or references to predefined splits. |
| Hardware Specification | Yes | to make the algorithms easier to run on our computing device, equipped with one NVIDIA RTX 3060 GPU and Intel i7-10700 CPU |
| Software Dependencies | No | The paper mentions software like PyTorch and specifies optimizers (Adam) but does not provide specific version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | Table 6: Hyperparameters used in experiments on DMControl (drq) |