Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Authors: Shariq Iqbal, Fei Sha
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have validated our approach on three simulated environments and tasks. The rest of the paper is organized as follows. In section 2, we discuss related work, followed by a detailed description of our approach in section 3. We report experimental studies in section 4 and conclude in section 5. |
| Researcher Affiliation | Collaboration | Shariq Iqbal 1 Fei Sha 1 2 1Department of Computer Science, University of Southern California (USC) 2On leave at Google AI (fsha@google.com). |
| Pseudocode | No | The paper describes the algorithms and their components but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | Code available at: https://github.com/shariqiqbal2810/MAAC |
| Open Datasets | Yes | All environments are implemented in the multi-agent particle environment framework2 introduced by Mordatch & Abbeel (2018), and extended by Lowe et al. (2017). ... Finally, we test on the Cooperative Navigation task proposed by Lowe et al. (2017) in order to demonstrate the general effectiveness of our method on a benchmark multi-agent task. |
| Dataset Splits | No | The paper mentions using specific environments for experiments, but it does not provide explicit details about dataset splits (e.g., percentages or counts for training, validation, or test sets) to enable reproduction of data partitioning. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | Full training details and hyperparameters can be found in the supplementary material. |