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..
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Authors: Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically show that our method outperforms other existing distribution-based algorithms in various environments including Atari 55 games. |
| Researcher Affiliation | Collaboration | Taehyun Cho1, Seungyub Han1,3, Heesoo Lee1, Kyungjae Lee2, Jungwoo Lee1 1 Seoul National University, 2 Chung-Ang University, 3 Hodoo AI Labs |
| Pseudocode | Yes | Algorithm 1 Perturbed QR-DQN (PQR) |
| Open Source Code | No | The paper references third-party codebases like DQN Zoo and Dopamine for comparisons, but does not provide an explicit statement or link for the open-source code of their own proposed method (PQR). |
| Open Datasets | Yes | Finally, we empirically show that our method outperforms other existing distribution-based algorithms in various environments including Atari 55 games. |
| Dataset Splits | No | The paper mentions using standard benchmark environments like Atari and N-Chain but does not explicitly provide training, validation, and test split percentages or sample counts within the text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Table 2: Table of hyperparameter setting |