Mjx is a Japanese Mahjong (riichi Mahjong) simulator. Mjx works as a game server as Mjai, a popular Mahjong simulator, to evaluate Mahjong AIs but have additional features:
- Fast (100x faster than Mjai)
- Exact Tenhou compatibility (Mjx is validated with numerous Tenhou game logs)
- Gym-like API
- Easy distributed computing (available for large-scale RL and evaluation thanks to gRPC)
- Mjai compatible (mjx_mjai_translater)
- Beautiful visualization
$ pip install mjx
Requirements. Mjx supports Python3.7 or later in Linux and macOS Intel (10.15 or later).
Currently Windows and macOS Apple Silicon are NOT supported.
Contributions for supporting Windows and macOS Apple Silicon are more than welcome!
import mjx
from mjx.agents import RandomAgent
agent = RandomAgent()
env = mjx.MjxEnv()
obs_dict = env.reset()
while not env.done():
actions = {player_id: agent.act(obs)
for player_id, obs in obs_dict.items()}
obs_dict = env.step(actions)
returns = env.rewards()| Server | Client |
|---|---|
import random
import mjx
class RandomAgent(mjx.Agent):
def __init__(self):
super().__init__()
# When you use neural network models
# you may want to infer actions by batch
def act_batch(self, observations):
return [random.choice(obs.legal_actions())
for obs in observations]
agent = RandomAgent()
# act_batch is called instead of act
agent.serve("127.0.0.1:8080", batch_size=8) |
import mjx
host="127.0.0.1:8080"
mjx.run(
{f"player_{i}": host for i in range(4)},
num_games=1000,
num_parallels=16
) |
This sever usage uses gRPC. Thus, actually any programming language is available to develop your own Mahjong AI.
For Python, we provide a convinent wrapper mjx.Agent.serve().
Mjx is still under active development. APIs might change without notice before v1.0. Especially,
- Default behavior of
env.rewards(reward_type)andenv.done(done_type) - Protobuf schema
- Feature extraction APIs (currently, provided by
Observation.to_features())
We recommend you to develop Mjx inside a container. Easiest way is open this repository from VsCode. Feel free to mention to @sotetsuk if you have any questions.
MIT