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..
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Authors: Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems. |
| Researcher Affiliation | Collaboration | Sharan Vaswani Simon Fraser University EMAIL Amirreza Kazemi Simon Fraser University EMAIL Reza Babanezhad Samsung SAIT AI Lab, Montreal EMAIL Nicolas Le Roux Microsoft Research, Mila EMAIL |
| Pseudocode | Yes | Algorithm 1: Generic actor-critic algorithm |
| Open Source Code | Yes | Code to reproduce the experiments is available at https://github.com/amirrezakazemi/ACPG |
| Open Datasets | Yes | We consider two grid-world environments, namely Cliff World [53] and Frozen Lake [6] |
| Dataset Splits | No | The paper describes using Monte-Carlo rollouts and training settings but does not specify explicit train/validation/test dataset splits with percentages or counts, which is typical for supervised learning but less so for RL environments where continuous interaction is common. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using the "gym framework [6]" but does not specify version numbers for Python, PyTorch, or any other critical software libraries or dependencies. Table 1 and 2 list parameter ranges but not software versions. |
| Experiment Setup | Yes | Table 1: Parameters for the Cliff World environment; Table 2: Parameters for the Frozen Lake environment. These tables include specific values/ranges for parameters like '# of samples', 'length of episode', 'mc', 'ma', 'Armijo max step-size', 'η', and 'c'. |