Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Authors: Fabian Akkerman, Julius Luy, Wouter van Heeswijk, Maximilian Schiffer
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our method by benchmarking it against three state-of-the-art approaches designed for large discrete action spaces across three distinct environments. Our results show that DNC matches or outperforms state-of-the-art approaches while being computationally more efficient. |
| Researcher Affiliation | Academia | 1 University of Twente (7500 AE Enschede, The Netherlands) 2 School of Management, Technical University of Munich (80333 Munich, Germany) 3 Munich Data Science Institute, Technical University of Munich (85748 Garching, Germany) |
| Pseudocode | Yes | Algorithm 1 Dynamic Neighborhood Construction |
| Open Source Code | Yes | The code for running DNC and all benchmarks on the studied domains can be found in the attached supplement and at: https://github.com/tum BAIS/ dynamic Neighborhood Construction. |
| Open Datasets | Yes | First, we consider a maze environment, which is used as a testbed in state-of-the-art works on LDAS (cf. Chandak et al., 2019a). Second, we consider a joint inventory replenishment problem (cf. Vanvuchelen et al., 2022). Third, we consider a well-known dynamic job-shop scheduling problem (see, e.g., Zhang et al., 2020a; Wu & Yan, 2023). |
| Dataset Splits | No | The paper describes training within reinforcement learning environments, using 'episodes' and 'timesteps', but does not specify explicit training/validation/test dataset splits with percentages or sample counts in the way typical for supervised learning on fixed datasets. |
| Hardware Specification | Yes | Our experiments are conducted on a high-performance cluster with 2.6Ghz CPUs with 56 threads and 64gb RAM per node. |
| Software Dependencies | No | The algorithms are coded in Python 3 and we use Py Torch to construct neural network architectures (Paszke et al., 2019). It mentions Python 3 but does not specify the version of PyTorch, which is a key software component. |
| Experiment Setup | Yes | In this section, we detail the hyperparameter settings used across the different environments. To this end, we provide an overview of hyperparameter settings in Table 2 and discuss specific settings that we use over all environments. |