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..
On the Convergence of SARSA with Linear Function Approximation
Authors: Shangtong Zhang, Remi Tachet Des Combes, Romain Laroche
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use a diagnostic MDP from Gordon (1996) (Figure 1) to illustrate the chattering of linear SARSA. Gordon (1996) tested the ϵ-greedy policy (2), which is not continuous. We further test the ϵ-softmax policy (3), whose Lipschitz constant is inversely proportional to the temperature ι. When ι approaches 0, the ϵ-softmax policy approaches the ϵ-greedy policy. We run Algorithm 1 in this MDP with CΓ = ∞, i.e., there is no projection. Following Gordon (1996), we set ϵ = 0.1, γ = 1.0, and αt = 0.01/t. As discussed in Gordon (1996), using a smaller discount factor or a decaying learning rate only slows down the chattering but the chattering always occurs. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Virginia, United States 2Alpaca ML 3Unemployed. Correspondence to: Shangtong Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 SARSA with linear function approximation |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use a diagnostic MDP from Gordon (1996) (Figure 1) to illustrate the chattering of linear SARSA. |
| Dataset Splits | No | The paper describes experiments on a diagnostic MDP (reinforcement learning environment) rather than a pre-split dataset. It does not mention any training, validation, or test data splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | Following Gordon (1996), we set ϵ = 0.1, γ = 1.0, and αt = 0.01/t. As discussed in Gordon (1996), using a smaller discount factor or a decaying learning rate only slows down the chattering but the chattering always occurs. We further fix ι to be 0.01 and test reward with different magnitudes. |