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.
Count-Based Exploration in Feature Space for Reinforcement Learning
Authors: Jarryd Martin, Suraj Narayanan S., Tom Everitt, Marcus Hutter
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,797 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,176 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 | Our evaluation demonstrates that this simple approach achieves near state-of-the-art performance on highdimensional RL benchmarks. |
| Researcher Affiliation | Academia | Jarryd Martin, Suraj Narayanan S., Tom Everitt, Marcus Hutter Research School of Computer Science, Australian National University, Canberra EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Reinforcement Learning with LFA and φ-EB. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or link to a code repository. |
| Open Datasets | Yes | We evaluate our algorithm on five games from the Arcade Learning Environment (ALE), which has recently become a standard high-dimensional benchmark for RL [Bellemare et al., 2013]. |
| Dataset Splits | No | The paper mentions training and evaluation (testing) frames and episodes but does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Sarsa(λ) and a Blob-PROST feature set but does not provide specific version numbers for any software, libraries, or frameworks. |
| Experiment Setup | Yes | The β coefficient in the φ-exploration bonus was set to 0.05 for all games, after a coarse parameter search. This search was performed once, across a range of ALE games, and a value was chosen for which the agent achieved good scores in most games. The parameters for the Sarsa(λ) algorithm are set to the same values as in [Liang et al., 2016]. |