Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Authors: Sultan J. Majeed, Marcus Hutter8874-8883
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we provide explicit and exact constructions and equivalence proofs for all quantities of interest for arbitrary history-based processes. |
| Researcher Affiliation | Collaboration | Sultan J. Majeed1*, Marcus Hutter2 1,2Research School of Computer Science, ANU, Canberra 2Google Deep Mind, London |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit statement) for open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, such as hyperparameter values or training configurations. |