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..
A Policy Gradient Method for Confounded POMDPs
Authors: Mao Hong, Zhengling Qi, Yanxun Xu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of Algorithm 1 by conducting a simulation study using RKHS endowed with a Gaussian kernel. Details of of the simulation setup can be found in Appendix L. Figure 2 summarizes the performance of three methods: the proposed method, the naive method and behavioral cloning. |
| Researcher Affiliation | Academia | Mao Hong Johns Hopkins University EMAIL Zhengling Qi George Washington University EMAIL Yanxun Xu Johns Hopkins University EMAIL |
| Pseudocode | Yes | Algorithm 1 Policy gradient ascent for POMDP in offline RL |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper states: 'We evaluate the performance of Algorithm 1 by conducting a simulation study'. It then describes how the data is generated: 'Simulated data are generated by assuming S0 unif( 2, 2), O0 0.8N(S0, 0.1) + 0.2N( S0, 0.1), S1 N(S0, 0.1), S2 N(S1A1, 0.01), πb(+1 | St > 0) = πb( 1 | St < 0) = 0.3, Ot 0.8N(St, 0.1) + 0.2N( St, 0.1), Rt(St, At) = 2 1 + exp( 4St At) 1, πθt(At | Ot) exp(θ t ϕt(At, Ot)), where ϕt,1(at, ot) := 2ot I(at > 0, ot > 0), ϕt,2(at, ot) := 2ot I(at < 0, ot > 0), ϕt,3(at, ot) := 2ot I(at > 0, ot < 0), ϕt,1(at, ot) := 2ot I(at < 0, ot < 0) for t = 1, 2.' This is a description of data generation, not a publicly available dataset with a link or citation. |
| Dataset Splits | No | The paper describes a simulation study where data is generated for the experiment, but it does not specify any training/validation/test dataset splits or mention cross-validation. The phrase 'offline data' is used to refer to the generated dataset used for learning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments or simulations. |
| Software Dependencies | No | The paper mentions 'RKHS endowed with a Gaussian kernel' in the simulation details, but it does not specify any particular software libraries, frameworks, or their version numbers (e.g., Python, PyTorch, TensorFlow, scikit-learn) that would be needed for replication. |
| Experiment Setup | Yes | The paper provides specific experimental setup details such as the total length of the horizon T = 2, the number of samples N = 10000, and the number of iterations K = 60 for the gradient ascent algorithm. It also mentions 'step sizes {ηk}K 1 k=0' and 'tuning parameters λN, µN, ξN'. |