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..
Set-membership Belief State-based Reinforcement Learning for POMDPs
Authors: Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our method for several challenging control tasks in this section. Our experiments aim to answer the following questions: First, can SBRL algorithm achieve good results in both partially observable and uncertain environments? Second, can SBM maintain accurate belief states to provide a reasonable basis for agents decision-making under uncertain and partially observable environments? |
| Researcher Affiliation | Academia | 1School of Computer and Information Technology, Shanxi University, Taiyuan 030006. PR. China. Correspondence to: Jiye Liang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 SBRL algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Mountain Hike is a continuous control environment with observation uncertainty where an agent navigates on a fixed 20 × 20 map, introduced by (Igl et al., 2018) to demonstrate the benefit of belief tracking for POMDP RL. |
| Dataset Splits | No | The paper uses standard benchmark environments but does not explicitly provide specific training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | No | We train SBRL and baselines with similar network architecture and hyperparameters as the original DPFRL implementation. |