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#!/usr/bin/env python3
import random
import numpy as np
import gym
from gym_minigrid.register import env_list
from gym_minigrid.minigrid import Grid, OBJECT_TO_IDX
# Test specifically importing a specific environment
from gym_minigrid.envs import DoorKeyEnv
# Test importing wrappers
from gym_minigrid.wrappers import *
##############################################################################
print('%d environments registered' % len(env_list))
for env_idx, env_name in enumerate(env_list):
print('testing {} ({}/{})'.format(env_name, env_idx+1, len(env_list)))
# Load the gym environment
env = gym.make(env_name)
env.max_steps = min(env.max_steps, 200)
env.reset()
env.render('rgb_array')
# Verify that the same seed always produces the same environment
for i in range(0, 5):
seed = 1337 + i
env.seed(seed)
grid1 = env.grid
env.seed(seed)
grid2 = env.grid
assert grid1 == grid2
env.reset()
# Run for a few episodes
num_episodes = 0
while num_episodes < 5:
# Pick a random action
action = random.randint(0, env.action_space.n - 1)
obs, reward, done, info = env.step(action)
# Validate the agent position
assert env.agent_pos[0] < env.width
assert env.agent_pos[1] < env.height
# Test observation encode/decode roundtrip
img = obs['image']
grid, vis_mask = Grid.decode(img)
img2 = grid.encode(vis_mask=vis_mask)
assert np.array_equal(img, img2)
# Test the env to string function
str(env)
# Check that the reward is within the specified range
assert reward >= env.reward_range[0], reward
assert reward <= env.reward_range[1], reward
if done:
num_episodes += 1
env.reset()
env.render('rgb_array')
# Test the close method
env.close()
env = gym.make(env_name)
env = ReseedWrapper(env)
for _ in range(10):
env.reset()
env.step(0)
env.close()
env = gym.make(env_name)
env = ImgObsWrapper(env)
env.reset()
env.step(0)
env.close()
# Test the fully observable wrapper
env = gym.make(env_name)
env = FullyObsWrapper(env)
env.reset()
obs, _, _, _ = env.step(0)
assert obs['image'].shape == env.observation_space.spaces['image'].shape
env.close()
# RGB image observation wrapper
env = gym.make(env_name)
env = RGBImgPartialObsWrapper(env)
env.reset()
obs, _, _, _ = env.step(0)
assert obs['image'].mean() > 0
env.close()
env = gym.make(env_name)
env = FlatObsWrapper(env)
env.reset()
env.step(0)
env.close()
env = gym.make(env_name)
env = ViewSizeWrapper(env, 5)
env.reset()
env.step(0)
env.close()
# Test the wrappers return proper observation spaces.
wrappers = [
RGBImgObsWrapper,
RGBImgPartialObsWrapper,
OneHotPartialObsWrapper
]
for wrapper in wrappers:
env = wrapper(gym.make(env_name))
obs_space, wrapper_name = env.observation_space, wrapper.__name__
assert isinstance(
obs_space, spaces.Dict
), "Observation space for {0} is not a Dict: {1}.".format(
wrapper_name, obs_space
)
# This should not fail either
ImgObsWrapper(env)
env.reset()
env.step(0)
env.close()
##############################################################################
print('testing agent_sees method')
env = gym.make('MiniGrid-DoorKey-6x6-v0')
goal_pos = (env.grid.width - 2, env.grid.height - 2)
# Test the "in" operator on grid objects
assert ('green', 'goal') in env.grid
assert ('blue', 'key') not in env.grid
# Test the env.agent_sees() function
env.reset()
for i in range(0, 500):
action = random.randint(0, env.action_space.n - 1)
obs, reward, done, info = env.step(action)
grid, _ = Grid.decode(obs['image'])
goal_visible = ('green', 'goal') in grid
agent_sees_goal = env.agent_sees(*goal_pos)
assert agent_sees_goal == goal_visible
if done:
env.reset()
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