Multi-Agent Generative Adversarial Imitation Learning
Authors: Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We evaluate the performance of (centralized, decentralized, and zero-sum versions) of MAGAIL under two types of environments. One is a particle environment which allows for complex interactions and behaviors; the other is a control task, where multiple agents try to cooperate and move a plank forward. We collect results by averaging over 5 random seeds. Our implementation is based on Open AI baselines [33]; please refer to Appendix C for implementation details3. |
| Researcher Affiliation | Academia | Jiaming Song Stanford University tsong@cs.stanford.edu Hongyu Ren Stanford University hyren@cs.stanford.edu Dorsa Sadigh Stanford University dorsa@cs.stanford.edu Stefano Ermon Stanford University ermon@cs.stanford.edu |
| Pseudocode | Yes | We outline the algorithm Multi-Agent GAIL (MAGAIL) in Appendix B. |
| Open Source Code | Yes | 3Code for reproducing the experiments are in https://github.com/ermongroup/multiagent-gail. |
| Open Datasets | Yes | We first consider the particle environment proposed in [14], which consists of several agents and landmarks. |
| Dataset Splits | No | The paper mentions using "100 to 400 episodes of expert demonstrations, each with 50 timesteps" but does not provide specific train/validation/test dataset splits or cross-validation details for these demonstrations. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments, such as GPU models, CPU models, or cloud computing specifications. |
| Software Dependencies | No | Our implementation is based on Open AI baselines [33]; please refer to Appendix C for implementation details3." (The reference [33] is 'Openai baselines. https://github.com/openai/baselines, 2017.') This mentions a software library but does not provide specific version numbers for it or other key dependencies. |
| Experiment Setup | Yes | We collect results by averaging over 5 random seeds." and "We consider 100 to 400 episodes of expert demonstrations, each with 50 timesteps, which is close to the amount of timesteps used for the control tasks in [16]." and "Following [34], we pretrain our Multi-Agent GAIL methods and the GAIL baseline using behavior cloning as initialization to reduce sample complexity for exploration. |