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..
Variational Bayesian Reinforcement Learning with Regret Bounds
Authors: Brendan O'Donoghue
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we compare the performance of both the temperature scheduled and optimized temperature variants of K-learning against several other methods in the literature. |
| Researcher Affiliation | Industry | Brendan O Donoghue Deep Mind, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 K-learning for episodic MDPs |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | We consider a small tabular MDP called Deep Sea [39] shown in Figure 1 |
| Dataset Splits | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] These experiments involved no training on external data. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Included in appendix. |
| Software Dependencies | Yes | SCS: Splitting conic solver, version 2.0.2. https://github.com/cvxgrp/scs, Nov. 2017. |
| Experiment Setup | Yes | We compare two dithering approaches, Q-learning with epsilongreedy (ϵ = 0.1) and soft-Q-learning [18] (τ = 0.05), against principled exploration strategies RLSVI [39], UCBVI [7], optimistic Q-learning (OQL) [23], BEB [24], Thompson sampling [38] and two variants of K-learning, one using the τt schedule (10) and the other using the optimal choice τ t from solving (11). |