Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Softmax Deep Double Deterministic Policy Gradients
Authors: Ling Pan, Qingpeng Cai, Longbo Huang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on challenging continuous control tasks, and results show that SD3 outperforms state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Ling Pan1, Qingpeng Cai2, Longbo Huang1 1Institute for Interdisciplinary Information Sciences, Tsinghua University EMAIL, EMAIL 2Alibaba Group EMAIL |
| Pseudocode | Yes | Algorithm 1 SD3 |
| Open Source Code | Yes | Details for hyperparameters are in Appendix E.1, and the implementation details are publicly available at https://github.com/ling-pan/SD3. |
| Open Datasets | Yes | We conduct extensive experiments in standard continuous control tasks from Open AI Gym [6] to evaluate the SD3 algorithm. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It uses standard environments but does not detail their splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Open AI Gym, Mu Jo Co, Box2d, and refers to implementations of DDPG and TD3, but it does not provide specific version numbers for these or any other ancillary software components needed to replicate the experiment. |
| Experiment Setup | Yes | For the softmax operator in SD3, the number of noises to sample K is 50, and the parameter β is mainly chosen from {10 3, 5 10 3, 10 2, 5 10 2, 10 1, 5 10 1} using grid search. All other hyperparameters of SD3 are set to be the same as the default setting for TD3 on all tasks except for Humanoid-v2... Details for hyperparameters are in Appendix E.1 |