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.
Towards Theoretical Understanding of Inverse Reinforcement Learning
Authors: Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 42,508 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,377 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 | In this paper, we make a step towards closing the theory gap of IRL in the case of finite-horizon problems with a generative model. We start by formally introducing the problem of estimating the feasible reward set, the corresponding PAC requirement, and discussing the properties of particular classes of rewards. Then, we provide the first minimax lower bound on the sample complexity for the problem of estimating the feasible reward set of order Ω H3SA δ S , being S and A the number of states and actions respectively, H the horizon, ϵ the desired accuracy, and δ the confidence. We analyze the sample complexity of a uniform sampling strategy (US-IRL), proving a matching upper bound up to logarithmic factors. |
| Researcher Affiliation | Academia | 1Dipartimento di Elettronica, Informazione e Bioingegneria. Politecnico di Milano. Milan, Italy. |
| Pseudocode | Yes | Algorithm 1. Uniform Sampling-IRL (US-IRL) for time-inhomogeneous (resp. time-homogeneous) transition models. |
| Open Source Code | No | The paper does not mention the release of open-source code or provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific dataset for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets with explicit training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or version numbers required for reproduction. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis rather than experimental setup details such as hyperparameters or training configurations. |