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..
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Authors: Filippos Christianos, Lukas Schäfer, Stefano Albrecht
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms several baselines and state-of-the-art algorithms by learning in fewer steps and converging to higher returns. |
| Researcher Affiliation | Academia | Filippos Christianos School of Informatics University of Edinburgh EMAIL Lukas Schäfer School of Informatics University of Edinburgh EMAIL Stefano V. Albrecht School of Informatics University of Edinburgh EMAIL |
| Pseudocode | Yes | Algorithm 1 Shared Experience Actor-Critic Framework |
| Open Source Code | Yes | We provide open-source implementations of SEAC in www.github.com/uoe-agents/seac |
| Open Datasets | Yes | We provide open-source implementations of SEAC in www.github.com/uoe-agents/seac and our two newly developed multi-agent environments: www.github.com/uoe-agents/lb-foraging (LBF) and www.github.com/uoe-agents/robotic-warehouse (RWARE). |
| Dataset Splits | No | The paper mentions "hyperparameter tuning for IAC on RWARE" but does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) within the main text. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., specific GPU/CPU models, memory amounts) used to run the experiments. |
| Software Dependencies | No | The paper discusses algorithms and frameworks like AC, DQN, PyTorch (implicitly through citations), but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | For all tested algorithms, we implement AC using n-step returns and synchronous environments [22]. Specifically, 5-step returns were used and four environments were sampled and passed in batches to the optimiser. An entropy regularisation term was added to the final policy loss [22], computing the entropy of the policy of each individual agent. Hence, the entropy term of agent i, given by H(π(oi t; φi)), only considers its own policy. High computational requirements in terms of environment steps only allowed hyperparameter tuning for IAC on RWARE; all tested AC algorithms use the same hyperparameters (see Appendix B). |