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.
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
Authors: Riccardo Zamboni, Alberto Maria Metelli, Marcello Restelli
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,075 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,346 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report the results of some illustrative numerical simulations, showing that the proposed algorithm matches the expected theoretical behavior and highlighting the relationship between aggregations and sample regimes. |
| Researcher Affiliation | Academia | Riccardo Zamboni DEIB, Politecnico di Milano Milan, Italy EMAIL Alberto Maria Metelli DEIB, Politecnico di Milano Milan, Italy EMAIL Marcello Restelli DEIB, Politecnico di Milano Milan, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Distributional Max-Ent Policy Evaluation... Algorithm 2 Distributional Max-Ent Progressive Factorization |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code, nor does it provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes a custom-designed 'rectangular Grid World' for its simulations but does not provide concrete access information (link, DOI, repository, or formal citation) for it to be considered a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Finally, the value of the partition splitting K is set to 2, to reduce the exponential search space of all possible uniform partitions, the discount factor γ is set to 0.98 and the confidence δ to 0.1, the results are averaged over 10 rounds with the respective standard deviation. |