run_tests.py

#!/usr/bin/env python3

import os
import numpy as np
import gym
import gym_miniworld
from gym_miniworld.wrappers import PyTorchObsWrapper, GreyscaleWrapper

env = gym.make('MiniWorld-Hallway-v0')

# Try stepping a few times
for i in range(0, 10):
    obs, _, _, _ = env.step(0)

# Check that the human rendering resembles the agent's view
first_obs = env.reset()
first_render = env.render('rgb_array')
m0 = first_obs.mean()
m1 = first_render.mean()
assert m0 > 0 and m0 < 255
assert abs(m0 - m1) < 5

# Check that the observation shapes match in reset and step
second_obs, _, _, _ = env.step(0)
assert first_obs.shape == env.observation_space.shape
assert first_obs.shape == second_obs.shape

# Test the PyTorch observation wrapper
env = PyTorchObsWrapper(env)
first_obs = env.reset()
second_obs, _, _, _ = env.step(0)
assert first_obs.shape == env.observation_space.shape
assert first_obs.shape == second_obs.shape

# Basic collision detection test
# Make sure the agent can never get outside of the room
env = gym.make('MiniWorld-OneRoom-v0')
for _ in range(30):
    env.reset()
    room = env.rooms[0]
    for _ in range(30):
        env.step(env.actions.move_forward)
        x, _, z = env.agent.pos
        assert x >= room.min_x and x <= room.max_x
        assert z >= room.min_z and z <= room.max_z

# Try loading each of the available environments
for env_id in gym_miniworld.envs.env_ids:
    if 'RemoteBot' in env_id:
        continue

    print('Testing "' + env_id + '"')
    env = gym.make(env_id)
    env.domain_rand = True
    # Try multiple random restarts
    for _ in range(15):
        env.reset()
        assert not env.intersect(env.agent, env.agent.pos, env.agent.radius)
        # Perform multiple random actions
        for _ in range(0, 20):
            action = env.rand.int(0, env.action_space.n)
            obs, reward, done, info = env.step(action)
            if done:
                env.reset()