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..
On Bonus Based Exploration Methods In The Arcade Learning Environment
Authors: Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on MONTEZUMA S REVENGE, Bellemare et al. s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. |
| Researcher Affiliation | Collaboration | Adrien Ali Ta ıga MILA, Universit e de Montr eal Google Brain William Fedus MILA, Universit e de Montr eal Google Brain Marlos C. Machado Google Brain Aaron Courville MILA, Universit e de Montr eal Marc G. Bellemare Google Brain |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using 'the Dopamine framework (Castro et al., 2018)' for its implementation but does not provide a link or explicit statement about the availability of its own specific source code for the described methodology. |
| Open Datasets | Yes | Arcade Learning Environment (ALE; Bellemare et al., 2013). ... the whole Atari 2600 suite. |
| Dataset Splits | No | The paper discusses 'training' and 'evaluation' and mentions tuning hyperparameters on MONTEZUMA S REVENGE, but it does not provide specific details on a separate validation dataset split (percentages, sample counts, or clear designation of a validation set) for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using the 'Dopamine framework (Castro et al., 2018)' and 'Rainbow implementation', and refers to 'Adam (Kingma & Ba, 2014)', but it does not specify version numbers for any of the software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | Appendix A.1 'RAINBOW AND ATARI PREPROCESSING' and A.2 'HYPERPARAMETER TUNING ON MONTEZUMA S REVENGE' provide specific hyperparameter values: 'Discount factor γ 0.99', 'Adam learning rate 6.25 10 5', 'Adam ϵ 1.5 10 4', 'Multi-step returns n 3', 'Distributional atoms 51', 'Distributional min/max values [-10, 10]', and details on tuning 'β' and 'α' for bonus-based methods. |