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..
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
Authors: Nishant Desai, Andrew Critch, Stuart J. Russell
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we implement a simple NRL agent and make empirical observations of this bet settling behavior. Our experiments are run in a modified version of the Frozen Lake environment in Open AI Gym [Brockman et al., 2016]. |
| Researcher Affiliation | Academia | Nishant Desai Center for Human-Compatible AI University of California, Berkeley EMAIL Andrew Critch Department of EECS University of California, Berkeley EMAIL Stuart Russell Computer Science Division University of California, Berkeley EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Our experiments are run in a modified version of the Frozen Lake environment in Open AI Gym [Brockman et al., 2016]. |
| Dataset Splits | No | The paper describes a reinforcement learning setup using a modified Frozen Lake environment. It does not specify explicit training, validation, and test dataset splits as typically found in supervised learning contexts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Open AI Gym [Brockman et al., 2016]' and 'point-based value iteration [Pineau et al., 2003]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | After running point-based value iteration [Pineau et al., 2003] with a belief set of 331 points, we execute the resulting policy in this environment. The agent is initialized with initial belief state w1, corresponding to a subjective belief that the agent is in Principal 1 s MDP, M1, with probability w1 and Principal 2 s MDP, M2, with probability 1 w1 = w2. |