Interactive Narrative Personalization with Deep Reinforcement Learning
Authors: Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {pwang8, jprowe, wmin, bwmott, lester}@ncsu.edu |
| 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. |