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..
Dynamic allocation of limited memory resources in reinforcement learning
Authors: Nisheet Patel, Luigi Acerbi, Alexandre Pouget
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards. |
| Researcher Affiliation | Academia | Nisheet Patel Department of Basic Neurosciences University of Geneva EMAIL Luigi Acerbi Department of Computer Science University of Helsinki EMAIL Alexandre Pouget Department of Basic Neurosciences University of Geneva EMAIL |
| Pseudocode | Yes | Algorithm 1: Dynamic Resource Allocator (DRA) |
| Open Source Code | Yes | Code to run DRA and reproduce our results is available at https://github.com/nisheetpatel/ Dynamic Resource Allocator. |
| Open Datasets | Yes | First, we consider the grid-world adapted from Mattar and Daw [16] and depicted in Fig. 1a. Next, we test DRA on the mountain car problem [39] |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test splits by percentages or sample counts. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU, or memory used for the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | Set hyper-parameters θ = (α1, α2, β, γ, λ) Initialize q, σ, table of memories = s, a, r, s , N( qsa, σ2 sa I) |S A| |