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..
Lexicographic Multi-Objective Reinforcement Learning
Authors: Joar Skalse, Lewis Hammond, Charlie Griffin, Alessandro Abate
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we evaluate our algorithms empirically. We first show how the learning time of LRL scales with the number of reward functions. We then compare the performance of VB-LRL and PB-LRL against that of other algorithms for solving constrained RL problems. Further experimental details and additional experiments are described in the supplementary material, and documented in our codebase. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1 Lexicographic ϵ-Greedy, Algorithm 2 Value-Based Lexicographic RL, Algorithm 3 Policy-Based Lexicographic RL |
| Open Source Code | Yes | Further experimental details and additional experiments are described in the supplementary material, and documented in our codebase.5 Available at https://github.com/lrhammond/lmorl. |
| Open Datasets | Yes | The Cart Safe environment from gym-safety6 is a version of the classic Cart Pole environment.6 Available at https://github.com/jemaw/gym-safety. The Grid Nav environment, again from gym-safety (based on an environment in [Chow et al., 2018]), is a large gridworld... Finally, in the Intersection environment from highway-env7 the agent must guide a car through an intersection with dense traffic.7 Available at https://github.com/eleurent/highway-env |
| Dataset Splits | No | The paper mentions training models and evaluating performance but does not specify any explicit train/validation/test dataset splits or their percentages/counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions software environments like 'gym-safety' and 'highway-env' but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper provides some high-level experimental details such as environment names and the number of runs, but it does not include concrete hyperparameter values, specific training configurations, or system-level settings within the main text. It defers further details to supplementary material. |