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..
Task-agnostic Exploration in Reinforcement Learning
Authors: Xuezhou Zhang, Yuzhe Ma, Adish Singla
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an efficient task-agnostic RL algorithm, UCBZERO, that finds ε-optimal policies for N arbitrary tasks after at most O(log(N)H5SA/ε2) exploration episodes, where H is the episode length, S is the state space size, and A is the action space size. We also provide an Ω(log(N)H2SA/ε2) lower bound, showing that the log dependency on N is unavoidable. |
| Researcher Affiliation | Academia | Xuezhou Zhang UW-Madison EMAIL Yuzhe Ma UW-Madison EMAIL Adish Singla MPI-SWS EMAIL |
| Pseudocode | Yes | Algorithm 1 UCBZERO |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use any datasets for experiments, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setup involving dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct research or simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings, as it is a theoretical work. |