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..
The Uncertainty Bellman Equation and Exploration
Authors: Brendan O’Donoghue, Ian Osband, Remi Munos, Vlad Mnih
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Substituting our UBE-exploration strategy for ϵ-greedy improves DQN performance on 51 out of 57 games in the Atari suite. |
| Researcher Affiliation | Industry | 1Deep Mind. Correspondence to: Brendan O Donoghue <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 One-step UBE exploration with linear uncertainty estimates. |
| Open Source Code | No | The paper does not contain any explicit statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Here we present results of Algorithm (1) on the Atari suite of games (Bellemare et al., 2012). |
| Dataset Splits | Yes | Every 1M frames the agents were saved and evaluated (without learning) on 0.5M frames, where each episode is started from the random start condition described in (Mnih et al., 2015). |
| Hardware Specification | No | The paper mentions running experiments 'on a GPU' but does not specify any particular model or detailed hardware configuration. |
| Software Dependencies | No | The paper mentions software components like 'DQN' and 'RMSProp optimizer' but does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | Yes | The β constant in (3) was chosen to be 0.01 for all games, by a parameter sweep. We used the exact same network architecture, learning rate, optimizer, pre-processing and replay scheme as described in Mnih et al. (2015). For the uncertainty sub-network we used a single fully connected hidden layer with 512 hidden units followed by the output layer. We trained the uncertainty head using a separate RMSProp optimizer (Tieleman & Hinton, 2012) with learning rate 10^-3. |