Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
Authors: Jeremy McMahan
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 39,188 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,957 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is purely theoretical and does not include any experiments. We present a novel algorithm that efficiently computes near-optimal deterministic policies for constrained reinforcement learning (CRL) problems. Our approach combines three key ideas: (1) value-demand augmentation, (2) action-space approximate dynamic programming, and (3) time-space rounding. Our algorithm constitutes a fully polynomial-time approximation scheme (FPTAS) for any time-space recursive (TSR) cost criteria. |
| Researcher Affiliation | Academia | Jeremy Mc Mahan University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Reduction to RL, Algorithm 2 Augmented interaction, Algorithm 3 Approx Bellman Update, Algorithm 4 Approx Solve, Algorithm 5 Approximation Scheme, Algorithm 6 Approx Solve. |
| Open Source Code | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q5) |
| Open Datasets | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q4) |
| Dataset Splits | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q4) |
| Hardware Specification | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q4) |
| Software Dependencies | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q4) |
| Experiment Setup | No | Our paper is purely theoretical and does not include any experiments. (Neur IPS Paper Checklist, Q4) |