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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-task Batch Reinforcement Learning with Metric Learning
Authors: Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik Christensen, Hao Su
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our proposed algorithm (Sec. 5.1) and ablate the different design choices (Sec. 5.2). Sec. 5.3 shows that the multi-task policy can serve as a good initialization, significantly speeding up training on unseen tasks. |
| Researcher Affiliation | Collaboration | Jiachen Li1 Quan Vuong1 Shuang Liu1 Minghua Liu1 Kamil Ciosek2 Henrik Christensen1 Hao Su1 1UC San Diego 2 Microsoft Research Cambridge, UK EMAIL EMAIL |
| Pseudocode | Yes | Alg. 1 illustrates the pseudo-code for the second phase of the distillation procedure. Detailed pseudocode of the two-phases distillation procedures can be found in Appendix E. |
| Open Source Code | Yes | Website: https://sites.google.com/eng.ucsd.edu/multi-task-batch-reinforcement/home |
| Open Datasets | Yes | We evaluate in five challenging task distributions from Mu Jo Co [38] and a modified task distribution Umaze Goal-M from D4RL [39]. |
| Dataset Splits | No | The paper discusses training and testing on unseen tasks but does not specify a validation dataset split or strategy for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'Soft Actor Critic (SAC)', 'BCQ', and 'Open AI gym state' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | Appendix C provides all hyper-parameters. |