[python]mujoco_py安装后测试代码

mujoco_py测试： 

import mujoco_py
import os
#mj_path, _ = mujoco_py.utils.discover_mujoco()
mj_path = mujoco_py.utils.discover_mujoco() #注意不同版本可能返回参数不一样
xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')
model = mujoco_py.load_model_from_path(xml_path)
sim = mujoco_py.MjSim(model)

print(sim.data.qpos)
# [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

sim.step()
print(sim.data.qpos)
# [-2.09531783e-19  2.72130735e-05  6.14480786e-22 -3.45474715e-06
#   7.42993721e-06 -1.40711141e-04 -3.04253586e-04 -2.07559344e-04
#   8.50646247e-05 -3.45474715e-06  7.42993721e-06 -1.40711141e-04
#  -3.04253586e-04 -2.07559344e-04 -8.50646247e-05  1.11317030e-04
#  -7.03465386e-05 -2.22862221e-05 -1.11317030e-04  7.03465386e-05
#  -2.22862221e-05]

gym测试：

import gym
env = gym.make('Humanoid-v2')

from gym import envs
print(envs.registry.all())    # print the available environments

print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)

for i_episode in range(200):
    observation = env.reset()
    for t in range(100):
        env.render()
        print(observation)
        action = env.action_space.sample()    # take a random action
        observation, reward, done, info = env.step(action)
        if done:
            print("Episode finished after {} timesteps".format(t+1))
            break
env.close()

原文地址：https://blog.csdn.net/FL1623863129/article/details/140184262

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