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 [1].

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies

Authors: Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao

ICLR 2023 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The main focus of this paper was the theoretical analysis of NPG method.
Researcher Affiliation Collaboration Rui Yuan FAIR, Meta AI LTCI, Télécom Paris and Institut Polytechnique de Paris EMAIL Simon S. Du University of Washington EMAIL Robert M. Gower CCM, Flatiron Institute EMAIL Alessandro Lazaric FAIR, Meta AI EMAIL Lin Xiao FAIR, Meta AI EMAIL
Pseudocode Yes Algorithm 1: Natural policy gradient; Algorithm 2: Q-Natural policy gradient; Algorithm 3: Sampler for: (s, a) d θ(ν) and unbiased estimate b Qs,a(θ) of Qs,a(θ); Algorithm 4: Sampler for: (s, a) d θ(ν) and unbiased estimate b As,a(θ) of As,a(θ); Algorithm 5: NPG-SGD; Algorithm 6: Q-NPG-SGD
Open Source Code No The paper does not provide concrete access to source code for the methodology described.
Open Datasets No The paper is theoretical and does not describe experiments using publicly available datasets or provide access information for any dataset.
Dataset Splits No The paper is theoretical and does not describe experiments that would require dataset splits for training, validation, and testing.
Hardware Specification No The paper is theoretical and does not describe experiments that would require specific hardware for execution.
Software Dependencies No The paper is theoretical and does not describe experiments that would require specific ancillary software with version numbers.
Experiment Setup No The paper is theoretical and does not describe empirical experimental setups or hyperparameter details.