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..
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
Authors: Alekh Agarwal, Tong Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically verify the suboptimality bounds via some standard reinforcement learning problems in Section 5. In this section, we numerically evaluate the performance of MB-OPS and compare it against baselines on common benchmarks. |
| Researcher Affiliation | Academia | Ruichu Cai1, Zhiwen Wu1, Zhen Liu2, Xinwang Liu3,∗ 1 School of Computer Science and Engineering, South China University of Technology 2 Peng Cheng Laboratory, Shenzhen 3 National University of Defense Technology |
| Pseudocode | Yes | Algorithm 1 Model-Based Optimistic Posterior Sampling (MB-OPS) |
| Open Source Code | No | The paper does not provide explicit statements about the public release of source code or links to a repository. |
| Open Datasets | Yes | We consider three environments: River Swim, Chain, and Mountain Car, which are widely used benchmarks in reinforcement learning. |
| Dataset Splits | No | The paper describes reinforcement learning environments (River Swim, Chain, Mountain Car) where data is generated through interaction, but it does not specify traditional train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch x.x'). |
| Experiment Setup | Yes | For the River Swim environment, we set the discount factor γ = 0.99, exploration parameter c = 0.1, the number of episodes to 2000, and the number of steps in each episode to 50. We set the step size α = 0.01 for the Mountain Car problem and a smaller value α = 0.001 for the River Swim and Chain environments. |