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..
Mixing-Time Regularized Policy Gradient
Authors: Tetsuro Morimura, Takayuki Osogami, Tomoyuki Shirai
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments in Section 6 show that the proposed method outperforms conventional PGRL methods. |
| Researcher Affiliation | Collaboration | Tetsuro Morimura IBM Research Tokyo 5-6-52 Toyosu, Koto-ku Tokyo, Japan EMAIL Takayuki Osogami IBM Research Tokyo 5-6-52 Toyosu, Koto-ku Tokyo, Japan EMAIL Tomoyuki Shirai Kyushu University 744 Motooka, Nishi-ku Fukuoka, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1: An implementation of the mixing-time regularized policy gradient reinforcement learning |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | Yes | The task is a simple two-state MDP in (Kakade 2002) |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, or testing). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The hyper-parameters of those methods were tuned. The targeted average reward η , which is a hyper-parameter in the proposed method of Option 2, was set as η := 0.75 maxθ R2 η(θ) = 1.5. |