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..
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards
Authors: Rati Devidze, Parameswaran Kamalaruban, Adish Singla
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several environments with sparse/noisy reward signals demonstrate the effectiveness of EXPLORS. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Software Systems (MPI-SWS), Saarbrucken, Germany 2The Alan Turing Institute, London, UK |
| Pseudocode | Yes | Algorithm 1 Online Reward Shaping |
| Open Source Code | Yes | 1Github repo: https://github.com/machine-teaching-group/neurips2022_exploration-guided-reward-shaping. |
| Open Datasets | No | The paper describes custom environments (CHAIN, ROOM, LINEK) but does not provide concrete access information (link, DOI, specific citation) for these datasets to be publicly available. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes evaluation during training based on extrinsic rewards. |
| Hardware Specification | No | The paper states "Details are provided in appendices" regarding compute resources, but no specific hardware details are given in the main text. |
| Software Dependencies | No | The paper mentions using "tabular REINFORCE agent [7]" and "tabular Q-learning agent [7]", and a "neural REINFORCE agent", but does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We give an overview of main results here, and provide a more detailed description of the setup and additional implementation details in appendices. |