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..
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees
Authors: Daqian Shao, Marta Kwiatkowska
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Several experiments on various tabular MDP environments across different LTL tasks demonstrate the improved sample efficiency and optimal policy convergence. |
| Researcher Affiliation | Academia | Daqian Shao and Marta Kwiatkowska Department of Computer Science, University of Oxford, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: KC Q-learning from LTL Algorithm 2: CF+KC Q-learning from LTL |
| Open Source Code | Yes | The implementation of our algorithms and experiments can be found on Git Hub: https://github.com/shaodaqian/rl-from-ltl |
| Open Datasets | Yes | The second MDP environment is the 8 8 frozen lake environment from Open AI Gym [Brockman et al., 2016]. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits (e.g., percentages or sample counts) as is typical for static datasets. As a reinforcement learning paper, it describes training steps and episodes within environments rather than partitioning a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions several tools used (PRISM, Rabinizer 4, Open AI Gym) and Q-learning as the core method, but does not provide specific version numbers for the software dependencies used in their experiments. |
| Experiment Setup | Yes | We set the learning rate α = 0.1 and ϵ = 0.1 for exploration. We also set a relatively loose upper bound on rewards U = 0.1 and discount factor γ = 0.99 for all experiments to ensure optimality. [...] for experiments we opt for a specific reward function that linearly increases the reward for accepting states as the value of K increases, namely rn = U n/K n [0..K]. The Q function is optimistically initialized by setting the Q value for all available state-action pairs to 2U. All experiments are run 100 times, where we plot the average satisfaction probability with half standard deviation in the shaded area. |