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..
Iterative Empirical Game Solving via Single Policy Best Response
Authors: Max Smith, Thomas Anthony, Michael Wellman
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that these algorithms substantially reduce the amount of simulation during training required by PSRO, while producing equivalent or better solutions to the game. |
| Researcher Affiliation | Collaboration | Max Olan Smith University of Michigan EMAIL Thomas Anthony Deepmind EMAIL Michael P. Wellman University of Michigan EMAIL |
| Pseudocode | Yes | Algorithm 2: Mixed-Oracles |
| Open Source Code | No | The paper mentions using 'the DeepMind RL library for Agents. This library is open-source (github.com/deepmind/acme)', but does not provide a link or explicit statement about releasing the source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate our algorithms on the Gathering (Perolat et al., 2017) and Leduc Poker (Southey et al., 2005) games, both of which are commonly used in the multiagent reinforcement learning field. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or sample counts) needed to reproduce data partitioning. It describes evaluation strategies and hyperparameter selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'DeepMind RL library for Agents' and 'Acme' and algorithms like 'Double Q-Learning', 'IMPALA', 'DQN', 'MPO', and 'Adam optimizer', but does not provide specific version numbers for any of these components. |
| Experiment Setup | Yes | 300 hyperparameter settings are sampled in each environment. Complete details are provided in Section D. |