![]() ![]() InfoSet Number: the number of information sets Avg. We provide a complexity estimation for the games on several aspects. API documents are available at our website. ![]() Please refer to the Documents for general introductions. Run examples/leduc_holdem_human.py to play with the pre-trained Leduc Hold'em model: > Leduc Hold'em pre-trained model ![]() Leduc Hold'em as single-agent environment.We also recommend the following toy examples. import rlcardįrom _agent import RandomAgent Or you can directly install the package with pip install rlcard We recommend installing rlcard with pip as follow: git clone Make sure that you have Python 3.5+ and pip installed. RLCard is developed by DATA Lab at Texas A&M University. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. It supports multiple card environments with easy-to-use interfaces. RLCard is a toolkit for Reinforcement Learning (RL) in card games. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |