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..
Fear and Hope Emerge from Anticipation in Model-Based Reinforcement Learning
Authors: Thomas Moerland, Joost Broekens, Catholijn Jonker
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Test results in three known RL domains illustrate emotion dynamics, dependencies on policy and environmental stochasticity, and plausibility in individual Pacman game settings. |
| Researcher Affiliation | Academia | Thomas Moerland, Joost Broekens, and Catholijn Jonker Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands EMAIL |
| Pseudocode | Yes | Algorithm 1 Model-based reinforcement learning with emotion simulation. |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the methodology is available. |
| Open Datasets | Yes | We test the emotion models in three scenario s: the Taxi domain (4.1) for hope, joy and distress, the Cliff Walking scenario (4.2) for fear, and finally Pacman (4.3) for plausibility of signals in a more complex and partially observable task. The Taxi domain (figure 1, introduced in [Dietterich, 1998]). In the Cliff Walking scenario (figure 3, adopted from p.149 of [Sutton and Barto, 1998])... Pacman (figure 5, based on [Sequeira et al., 2014]). |
| Dataset Splits | No | The paper describes the experimental scenarios and training process (e.g., 'Pacman interact with the environment for 100000 iterations'), but it does not specify explicit training/validation/test dataset splits, which are more common in supervised learning contexts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | Results for UCT(N=300, dmax=7). The =0.05 agent has less exploration, which makes it more hopefull about reaching the target. Results for UCT(N = 18,dmax = 4) runs on a converged model. We modify equation 4 from a strict maximization to an -greedy policy with =0.10. Pacman interact with the environment for 100000 iterations ( linear decreasing from 1 to 0.05 in the first 30000 iterations). |