Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interactive Narrative Personalization with Deep Reinforcement Learning
Authors: Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the deep RL framework s performance with an educational interactive narrative, CRYSTAL ISLAND. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives. By evaluating the Q-network based interactive narrative planner in a widely used educational interactive narrative, CRYSTAL ISLAND, we demonstrate that deep RL yields more effective policies for interactive narrative personalization than commonly utilized linear RL techniques. |
| Researcher Affiliation | Academia | Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA EMAIL |
| 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 for source code. |
| Open Datasets | No | The paper mentions using a "human player interaction corpus for CRYSTAL ISLAND" generated from human subject studies but does not provide any access information (link, DOI, repository, or citation to a public source) for this dataset. |
| Dataset Splits | No | The paper states that data is divided into training (80%) and test (20%) sets but does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running experiments, only general mentions like "without GPU acceleration" and "on 4 threads". |
| Software Dependencies | No | The paper mentions the use of 'Adam' optimizer and 'LSTMs' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For all of these structures, Hidden Layer 1 has 64 neurons, and Hidden Layer 2 (either fully connected layer or LSTM) has 32 neurons. The advantage stream and state value stream both contain 32 neurons in dueling structures. Discount factor γ is set to 1. |