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..
Balancing Two-Player Stochastic Games with Soft Q-Learning
Authors: Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We contribute both theoretically and empirically. On the theory side, we show that games with soft Q-learning exhibit a unique value and generalise team games and zero-sum games far beyond these two extremes to cover a continuous spectrum of gaming behaviour. Experimentally, we show how tuning agents constraints affect performance and demonstrate, through a neural network architecture, how to reliably balance games with high-dimensional representations. |
| Researcher Affiliation | Industry | Jordi Grau-Moya, Felix Leibfried and Haitham Bou-Ammar PROWLER.io EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Two-Player Soft Q-Learning |
| Open Source Code | No | The paper provides a link for 'Game-play videos' but not for the source code of their methodology. 'Game-play videos can be found at https://sites.google.com/site/submission3591/.' |
| Open Datasets | No | The paper mentions a '5x6 grid-world' and 'the game Pong from the Roboschool package' as environments for experiments, but does not provide access information (link, citation) for a specific dataset used for training, as agents are trained in these environments. |
| Dataset Splits | No | The paper describes training in environments (grid-world and Pong) rather than using predefined datasets with explicit train/validation/test splits. No specific data splits were provided for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were mentioned. |
| Software Dependencies | No | The paper mentions using the 'Roboschool package' and 'ADAM optimizer' but does not specify their version numbers or any other software dependencies with version details. |
| Experiment Setup | Yes | For the low-dimensional experiments, 'For all experiments we used a high learning rate of α = 0.5'. For high-dimensional Pong experiments: 'We used a learning rate of 10^-4, the ADAM optimizer, a batch size of 32, and updated the target every 30000 training steps.' |