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.
Online Learning with Off-Policy Feedback in Adversarial MDPs
Authors: Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Papini, Alberto Maria Metelli, Nicola Gatti
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,053 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,974 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 | First, we present a lowerbound for the setting we propose, which shows that the optimal dependency of the sublinear regret is w.r.t. the dissimilarity between the optimal policy in hindsight and the colleague s policy. Then, we propose novel algorithms that, by employing pessimistic estimators commonly adopted in the offline reinforcement learning literature ensure sublinear regret bounds depending on the desired dissimilarity, even when the colleague s policy is unknown. |
| Researcher Affiliation | Academia | Politecnico di Milano |
| Pseudocode | Yes | Algorithm 1 Learner-Environment Interaction; Algorithm 2 Pessimistic Relative Entropy Policy Search (P-REPS); Algorithm 3 Pessimistic Relative Entropy Policy Search with unknown colleague policy (P-REPS+) |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention using specific datasets for training experiments. Therefore, it does not provide concrete access information for a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits. No information on training/test/validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for running experiments. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs rather than their implementation details. It does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. No specific experiment setup details are provided. |