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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound
Authors: Gal Dalal, Balazs Szorenyi, Gugan Thoppe3701-3708
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we provide convergence rate bounds for this suite of algorithms... Via comparable lower bounds, we show that these bounds are, in fact, tight. To the best of our knowledge, ours is the ๏ฌrst ๏ฌnite-time analysis which achieves these rates. Here, we obtain tight convergence rate estimates for the special class of linear two-timescale SA, which involves two interleaved update rules with distinct stepsize sequences. |
| Researcher Affiliation | Collaboration | Gal Dalal,1 Bal azs Sz or enyi,2 Gugan Thoppe3 1Technion, Israel Institute of Technology, Haifa, Israel; EMAIL 2Yahoo! Research, New York, NY, USA; EMAIL 3Duke University, Durham, NC, USA; EMAIL |
| Pseudocode | No | The paper provides mathematical update rules (equations 1, 2, 5, 6) but does not include structured pseudocode or algorithm blocks that are clearly labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that the code is released or available in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments using specific datasets; therefore, it does not provide concrete access information for a training dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, so it does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require specific hardware; therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments or implementations, so it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations. |