  We introduce a new virtual environment for simulating a card game known as
"Big 2". This is a four-player game of imperfect information with a relatively
complicated action space (being allowed to play 1,2,3,4 or 5 card combinations
from an initial starting hand of 13 cards). As such it poses a challenge for
many current reinforcement learning methods. We then use the recently proposed
"Proximal Policy Optimization" algorithm to train a deep neural network to play
the game, purely learning via self-play, and find that it is able to reach a
level which outperforms amateur human players after only a relatively short
amount of training time and without needing to search a tree of future game
states.
