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.
Incremental Learning of Planning Actions in Model-Based Reinforcement Learning
Authors: Jun Hao Alvin Ng, Ronald P. A. Petrick
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,374 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,994 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 | We evaluate our work with experimental results for three planning domains. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, Heriot-Watt University 2 School of Informatics, University of Edinburgh EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Incremental Learning Model |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the methodology. |
| Open Datasets | Yes | We used three planning domains: the Tireworld and Exploding Blocksworld domains from the International Probabilistic Planning Competition [Younes et al., 2005], and the Logistics domain. |
| Dataset Splits | No | The paper does not specify details about a validation dataset or explicit train/validation/test splits. |
| Hardware Specification | Yes | The machine used to run the experiments was a four core Intel(R) i5-6500 with 4 GB of RAM. |
| Software Dependencies | No | The paper mentions software like PPDDL and Gourmand but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In one trial of experiments, ten planning problems are attempted sequentially in an order of increasing scale (see Table 1) following the idea behind curriculum learning [Bengio et al., 2009]. We denote each attempt as an episode. Each trial starts with no prior knowledge; the prior action models for episode 1 are empty action models. Since the planning problems are probabilistic, 50 independent trials are conducted. |