mjx 0.0.11

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Requirements

• Ubuntu 20.04 or later
• MacOS 10.15 or later
• Python >= 3.7

Example

import mjx

agent = mjx.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()

Sever Usage

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(observation.legal_actions()) for obs in observations] agent = RandomAgent() agent.serve("127.0.0.1:8080", batch_size=8)	import mjx host="127.0.0.1" mjx.run( { "player_0": host, "player_1": host, "player_2": host, "player_3": host }, num_games=1000, num_parallels=16 )