def __init__(self, env_spec, env_space, e=0):
self.env_spec = env_spec
self.env_space = env_space
self.info_space = env_space.info_space
self.e = e
util.set_attr(self, self.env_spec)
self.name = self.env_spec['name']
self.body_e = None
self.nanflat_body_e = None # nanflatten version of bodies
self.body_num = None
worker_id = int(f'{os.getpid()}{self.e+int(ps.unique_id())}'[-4:])
self.u_env = UnityEnvironment(file_name=util.get_env_path(self.name),
worker_id=worker_id)
# spaces for NN auto input/output inference
logger.warn(
'Unity environment observation_space and action_space are constructed with invalid range. Use only their shapes.'
)
self.observation_spaces = []
self.action_spaces = []
for a in range(len(self.u_env.brain_names)):
observation_shape = (self.get_observable_dim(a)['state'], )
if self.get_brain(a).state_space_type == 'discrete':
observation_space = gym.spaces.Box(low=0,
high=1,
shape=observation_shape,
dtype=np.int32)
else:
observation_space = gym.spaces.Box(low=0.0,
high=1.0,
shape=observation_shape,
dtype=np.float32)
self.observation_spaces.append(observation_space)
if self.is_discrete(a):
action_space = gym.spaces.Discrete(self.get_action_dim(a))
else:
action_space = gym.spaces.Box(low=0.0,
high=1.0,
shape=(1, ),
dtype=np.float32)
self.action_spaces.append(action_space)
for observation_space, action_space in zip(self.observation_spaces,
self.action_spaces):
set_gym_space_attr(observation_space)
set_gym_space_attr(action_space)
# TODO experiment to find out optimal benchmarking max_timestep, set
# TODO ensure clock_speed from env_spec
self.clock_speed = 1
self.clock = Clock(self.clock_speed)
self.done = False
def __init__(self, spec, e=None, env_space=None):
super(UnityEnv, self).__init__(spec, e, env_space)
util.set_attr(self, self.env_spec, ['unity'])
worker_id = int(f'{os.getpid()}{self.e+int(ps.unique_id())}'[-4:])
self.u_env = UnityEnvironment(file_name=util.get_env_path(self.name),
worker_id=worker_id)
self.patch_gym_spaces(self.u_env)
self._set_attr_from_u_env(self.u_env)
if env_space is None: # singleton mode
pass
else:
self.space_init(env_space)
logger.info(util.self_desc(self))
def __init__(self, spec, env_space, e=0):
self.spec = spec
util.set_attr(self, self.spec)
self.name = self.spec['name']
self.env_space = env_space
self.e = e
self.body_e = None
self.flat_nonan_body_e = None # flatten_nonan version of bodies
worker_id = int(f'{os.getpid()}{self.e+int(_.unique_id())}'[-4:])
self.u_env = UnityEnvironment(file_name=util.get_env_path(self.name),
worker_id=worker_id)
# TODO experiment to find out optimal benchmarking max_timestep, set
# TODO ensure clock_speed from spec
self.clock_speed = 1
self.clock = Clock(self.clock_speed)
self.done = False
def __init__(self, env_spec, env_space, e=0):
self.env_spec = env_spec
self.env_space = env_space
self.info_space = env_space.info_space
self.e = e
util.set_attr(self, self.env_spec)
self.name = self.env_spec['name']
self.body_e = None
self.nanflat_body_e = None # nanflatten version of bodies
self.body_num = None
worker_id = int(f'{os.getpid()}{self.e+int(ps.unique_id())}'[-4:])
self.u_env = UnityEnvironment(file_name=util.get_env_path(self.name), worker_id=worker_id)
# spaces for NN auto input/output inference
logger.warn('Unity environment observation_space and action_space are constructed with invalid range. Use only their shapes.')
self.observation_spaces = []
self.action_spaces = []
for a in range(len(self.u_env.brain_names)):
observation_shape = (self.get_observable_dim(a)['state'],)
if self.get_brain(a).state_space_type == 'discrete':
observation_space = gym.spaces.Box(low=0, high=1, shape=observation_shape, dtype=np.int32)
else:
observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=observation_shape, dtype=np.float32)
self.observation_spaces.append(observation_space)
if self.is_discrete(a):
action_space = gym.spaces.Discrete(self.get_action_dim(a))
else:
action_space = gym.spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32)
self.action_spaces.append(action_space)
for observation_space, action_space in zip(self.observation_spaces, self.action_spaces):
set_gym_space_attr(observation_space)
set_gym_space_attr(action_space)
# TODO experiment to find out optimal benchmarking max_timestep, set
# TODO ensure clock_speed from env_spec
self.clock_speed = 1
self.clock = Clock(self.clock_speed)
self.done = False
def test_get_env_path():
assert 'node_modules/slm-env-3dball/build/3dball' in util.get_env_path(
'3dball')
def test_get_env_path():
assert 'node_modules/slm-env-3dball/build/3dball' in util.get_env_path(
'3dball')
'''
from slm_lab.lib import util
from unityagents import UnityEnvironment
import numpy as np
import time
# # Multiple env classes simultaneously
# env_path = environment.get_env_path('3dball')
# env_1 = UnityEnvironment(file_name=env_path, worker_id=1)
# env_path = environment.get_env_path('gridworld')
# env_2 = UnityEnvironment(file_name=env_path, worker_id=2)
# env_1.reset(train_mode=False)
# env_2.reset(train_mode=False)
env_path = util.get_env_path('gridworld')
# use train_mode = False to debug, i.e. render env at real size, real time
train_mode = False
# UnityEnvironment interfaces python with Unity,
# and contains brains for controlling connected agents.
env = UnityEnvironment(file_name=env_path)
print(str(env))
# get the default brain
default_brain = env.brain_names[0]
brain = env.brains[default_brain]
env_info = env.reset(train_mode=train_mode)[default_brain]
'''
is_continuous = (brain.action_space_type == 'continuous')
use_observations = (brain.number_observations > 0)
'''
from slm_lab.lib import util
from unityagents import UnityEnvironment
import numpy as np
import time
# # Multiple env classes simultaneously
# env_path = environment.get_env_path('3dball')
# env_1 = UnityEnvironment(file_name=env_path, worker_id=1)
# env_path = environment.get_env_path('gridworld')
# env_2 = UnityEnvironment(file_name=env_path, worker_id=2)
# env_1.reset(train_mode=False)
# env_2.reset(train_mode=False)
env_path = util.get_env_path('gridworld')
# use train_mode = False to debug, i.e. render env at real size, real time
train_mode = False
# UnityEnvironment interfaces python with Unity,
# and contains brains for controlling connected agents.
env = UnityEnvironment(file_name=env_path)
print(str(env))
# get the default brain
default_brain = env.brain_names[0]
brain = env.brains[default_brain]
env_info = env.reset(train_mode=train_mode)[default_brain]
'''
is_continuous = (brain.action_space_type == 'continuous')
use_observations = (brain.number_observations > 0)