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from gym.envs.registration import register
register(
id='MicropolisEnv-v0',
entry_point='gym_city.envs:MicropolisEnv',
kwargs={'MAP_X':14, 'MAP_Y':14}
)
register(
id='MicropolisPaintEnv-v0',
entry_point='gym_city.envs:MicropolisPaintEnv',
kwargs={'MAP_X':14, 'MAP_Y':14}
)
register(
id='MicropolisWalkEnv-v0',
entry_point='gym_city.envs:MicroWalkEnv',
)
register(
id='MicropolisArcadeEnv-v0',
entry_point='gym_city.envs:MicroArcadeEnv',
)
Environment ID that will compose a batch.
batch_size:
Number of independent environments to run.
parallel:
If True, the environment will be executed in different processes.
Returns:
The corresponding gym-compatible env_id to use.
"""
batch_env_id = "batch{}-".format(batch_size) + env_id
env_spec = spec(env_id)
entry_point = 'textworld.gym.envs:BatchEnv'
if parallel and batch_size > 1:
entry_point = 'textworld.gym.envs:ParallelBatchEnv'
register(
id=batch_env_id,
entry_point=entry_point,
max_episode_steps=env_spec.max_episode_steps,
nondeterministic=env_spec.nondeterministic,
reward_threshold=env_spec.reward_threshold,
# Setting the 'vnc' tag avoid wrapping the env with a TimeLimit wrapper. See
# https://github.com/openai/gym/blob/4c460ba6c8959dd8e0a03b13a1ca817da6d4074f/gym/envs/registration.py#L122
tags={"vnc": "foo"},
kwargs={'env_id': env_id, 'batch_size': batch_size}
)
return batch_env_id
def register_pddl_env(name, is_test_env, other_args):
dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "pddl")
domain_file = os.path.join(dir_path, "{}.pddl".format(name.lower()))
gym_name = name.capitalize()
problem_dirname = name.lower()
if is_test_env:
gym_name += 'Test'
problem_dirname += '_test'
problem_dir = os.path.join(dir_path, problem_dirname)
register(
id='PDDLEnv{}-v0'.format(gym_name),
entry_point='pddlgym.core:PDDLEnv',
kwargs=dict({'domain_file' : domain_file, 'problem_dir' : problem_dir,
**other_args}),
)
def _register():
##########################################
# Version 0 of env
##########################################
gym.envs.registration.register(
id='MarLo-CliffWalking-v0',
entry_point=MarloEnvBuilder,
kwargs={
"extra_params": {
}
def register_custom_envs():
global _REGISTERED
if _REGISTERED:
return
_REGISTERED = True
LOGGER.info("Registering multiworld pygame gym environments")
register(
id='Point2DLargeEnv-offscreen-v0',
entry_point='multiworld.envs.pygame.point2d:Point2DEnv',
tags={
'git-commit-hash': '166f0f3',
'author': 'Vitchyr'
},
kwargs={
'images_are_rgb': True,
'target_radius': 1,
'ball_radius': 1,
'render_onscreen': False,
},
)
register(
id='Point2DLargeEnv-onscreen-v0',
entry_point='multiworld.envs.pygame.point2d:Point2DEnv',
# MACROS
LEFT = 0
DOWN = 1
RIGHT = 2
UP = 3
# Key mapping
arrow_keys = {
'\x1b[A': UP,
'\x1b[B': DOWN,
'\x1b[C': RIGHT,
'\x1b[D': LEFT}
# Register FrozenLake with is_slippery False
register(
id='FrozenLake-v3',
entry_point='gym.envs.toy_text:FrozenLakeEnv',
kwargs={'map_name': '4x4', 'is_slippery': False}
)
env = gym.make('FrozenLake-v3')
env.render() # Show the initial board
while True:
# Choose an action from keyboard
key = inkey()
if key not in arrow_keys.keys():
print("Game aborted!")
break
action = arrow_keys[key]
from gym.envs.registration import register
register(
id='CartPoleContinuous-v0',
entry_point='env.cartpole_continuous:CartPoleContinuousEnv',
max_episode_steps=200,
reward_threshold=195.0,
)
from gym.envs.registration import register
register(
id='large-office-v0',
entry_point='eplus.envs:LargeOfficeEnv',
)
register(
id='data-center-v0',
entry_point='eplus.envs:DataCenterEnv'
)
# This is the original version, but not work on windows.
# ........
LEFT = 0
DOWN = 1
RIGHT = 2
UP = 3
arrow_keys = {
'\x1b[A' : UP,
'\x1b[B' : DOWN,
'\x1b[C' : RIGHT,
'\x1b[D' : LEFT
}
register(
id='FrozenLake-v3',
entry_point='gym.envs.toy_text:FrozenLakeEnv',
kwargs={'map_name' : '4x4', 'is_slippery': False}
)
env = gym.make('FrozenLake-v3')
env.render()
while True:
key = readchar.readkey()
if key not in arrow_keys.keys():
print("Game aborted!")
break
action = arrow_keys[key]
state, reward, done, info = env.step(action)
from gym.envs.registration import register
register(
id='deepracer-v0',
entry_point='gym_deepracer.envs:DeepRacerEnv',
)
register(
id='deepracerDiscrete-v0',
entry_point='gym_deepracer.envs:DeepRacerEnvDiscrete',
)