FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning
Authors: Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experimental results using the above toolkits to serve as the baselines for two-player competitive game settings. |
| Researcher Affiliation | Academia | 1Princeton University. Correspondence to: Wenzhe Li <wenzhe.li@princeton.edu>, Chi Jin <chij@princeton.edu>. |
| Pseudocode | Yes | Algorithm 1 Population-Based Methods for MGs |
| Open Source Code | Yes | Videos and code at https://sites.google.com/view/fightladder/home. |
| Open Datasets | No | The paper introduces Fight Ladder as a real-time fighting game platform and environment where observations are generated from game emulators, rather than using a pre-existing, fixed public dataset. |
| Dataset Splits | No | The paper discusses training steps and epochs, but does not provide explicit training, validation, and test dataset splits. |
| Hardware Specification | Yes | We trained all our agents on one server with 192 CPUs and 8 A6000 GPUs. |
| Software Dependencies | No | The paper mentions software like Gym and Stable-Baselines3, and uses ECOS, but does not provide specific version numbers for these software dependencies (e.g., 'Stable-Baselines3 version X.Y.Z'). |
| Experiment Setup | Yes | Table 6. Training hyperparameters for PPO, which is the backbone for both single-player and two-player algorithms in the experiment. Table 7. Training hyperparameters for FSP, PSRO, and League. |