Dynamic allocation of limited memory resources in reinforcement learning
Authors: Nisheet Patel, Luigi Acerbi, Alexandre Pouget
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 nisheet.patel@unige.ch Luigi Acerbi Department of Computer Science University of Helsinki luigi.acerbi@helsinki.fi Alexandre Pouget Department of Basic Neurosciences University of Geneva alexandre.pouget@unige.ch |
| 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| |