def test_julia_env(self):
env = JuliaEnv(env_id='DeterministicSingleStepDebugEnv',
env_params={},
using='AutoEnvs')
x = env.reset()
nx, r, t, info = env.step(1)
self.assertEqual(x, 0)
self.assertEqual(nx, 0)
self.assertEqual(r, 1)
self.assertEqual(t, True)
def test_multiagent_ngsim_env(self):
basedir = os.path.expanduser('~/.julia/packages/NGSIM/9OYUa/data')
filename = 'trajdata_i101_trajectories-0750am-0805am.txt'
filepaths = [os.path.join(basedir, filename)]
n_veh = 5
env = JuliaEnv(env_id='MultiagentNGSIMEnv',
env_params=dict(n_veh=n_veh,
trajectory_filepaths=filepaths,
H=200,
primesteps=50),
using='AutoEnvs')
low, high = env.action_space.low, env.action_space.high
env = TfEnv(env)
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32),
std_hidden_sizes=(32, 32),
adaptive_std=True,
output_nonlinearity=None,
learn_std=True)
baseline = ZeroBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
n_itr=1,
batch_size=1000,
sampler_args=dict(n_envs=n_veh))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
try:
algo.train(sess=sess)
except Exception as e:
self.fail('exception incorrectly raised: {}'.format(e))
def build_ngsim_env(
args,
exp_dir='/tmp',
alpha=0.001,
vectorize=False,
render_params=None,
videoMaking=False):
basedir = os.path.expanduser('~/.julia/packages/NGSIM/9OYUa/data')
filepaths = [os.path.join(basedir, args.ngsim_filename)]
if render_params is None:
render_params = dict(
viz_dir=os.path.join(exp_dir, 'imitate/viz'),
zoom=5.
)
env_params = dict(
trajectory_filepaths=filepaths,
H=args.env_H,
primesteps=args.env_primesteps,
action_repeat=args.env_action_repeat,
terminate_on_collision=False,
terminate_on_off_road=False,
render_params=render_params,
n_envs=args.n_envs,
n_veh=args.n_envs,
remove_ngsim_veh=args.remove_ngsim_veh,
reward=args.env_reward
)
# order matters here because multiagent is a subset of vectorized
# i.e., if you want to run with multiagent = true, then vectorize must
# also be true
if args.env_multiagent:
env_id = 'MultiagentNGSIMEnv'
if videoMaking:
print('RAUNAK BHATTACHARRYA VIDEO MAKER IS ON')
env_id='MultiagentNGSIMEnvVideoMaker'
alpha = alpha * args.n_envs
normalize_wrapper = vectorized_normalized_env
elif vectorize:
env_id = 'VectorizedNGSIMEnv'
alpha = alpha * args.n_envs
normalize_wrapper = vectorized_normalized_env
else:
env_id = 'NGSIMEnv'
normalize_wrapper = normalize_env
env = JuliaEnv(
env_id=env_id,
env_params=env_params,
using='AutoEnvs'
)
# get low and high values for normalizing _real_ actions
low, high = env.action_space.low, env.action_space.high
env = TfEnv(normalize_wrapper(env, normalize_obs=True, obs_alpha=alpha))
add_kwargs_to_reset(env)
return env, low, high
def build_ngsim_env(args,
exp_dir='/tmp',
alpha=0.001,
vectorize=False,
render_params=None):
basedir = os.path.expanduser('~/.julia/v0.6/NGSIM/data')
filepaths = [os.path.join(basedir, args.ngsim_filename)]
if render_params is None:
render_params = dict(viz_dir=os.path.join(exp_dir, 'imitate/viz'),
zoom=5.)
env_params = dict(trajectory_filepaths=filepaths,
H=args.env_H,
primesteps=args.env_primesteps,
action_repeat=args.env_action_repeat,
terminate_on_collision=False,
terminate_on_off_road=False,
render_params=render_params,
n_envs=args.n_envs,
n_veh=args.n_envs)
# order matters here because multiagent is a subset of vectorized
# i.e., if you want to run with multiagent = true, then vectorize must
# also be true
normalize_kwargs = dict(normalize_obs=True, obs_alpha=alpha)
if not args.adaptive_normalization:
# only apply clipping when running without adaptive normalization
normalize_kwargs[
'clip_std_multiple'] = args.normalize_clip_std_multiple
if args.env_multiagent:
env_id = 'MultiagentNGSIMEnv'
alpha = alpha * args.n_envs
normalize_wrapper = vectorized_normalized_env
elif vectorize:
env_id = 'VectorizedNGSIMEnv'
alpha = alpha * args.n_envs
normalize_wrapper = vectorized_normalized_env
else:
env_id = 'NGSIMEnv'
normalize_wrapper = normalize_env
if not args.adaptive_normalization:
raise NotImplementedError(
'''non vectorized env not implemented for
use without adaptive normalization''')
env = JuliaEnv(env_id=env_id, env_params=env_params, using='AutoEnvs')
# get low and high values for normalizing _real_ actions
low, high = env.action_space.low, env.action_space.high
env = TfEnv(normalize_wrapper(env, **normalize_kwargs))
add_kwargs_to_reset(env)
return env, low, high
def perf_ngsim_env_step():
basedir = os.path.expanduser('~/.julia/packages/NGSIM/9OYUa/data')
filename = 'trajdata_i101_trajectories-0750am-0805am.txt'
filepaths = [os.path.join(basedir, filename)]
env = JuliaEnv(env_id='NGSIMEnv',
env_params=dict(trajectory_filepaths=filepaths, ),
using='AutoEnvs')
n_steps = 10000
action = np.array([1., 0.])
env.reset()
with ContextTimer():
for _ in range(n_steps):
_, _, terminal, _ = env.step(action)
if terminal:
env.reset()
def test_ngsim_env(self):
basedir = os.path.expanduser('~/.julia/packages/NGSIM/9OYUa/data')
filename = 'trajdata_i80_trajectories-0400-0415.txt'
filepaths = [os.path.join(basedir, filename)]
env = JuliaEnv(env_id='NGSIMEnv',
env_params=dict(trajectory_filepaths=filepaths),
using='AutoEnvs')
x = env.reset()
nx, r, t, info = env.step(np.array([0., 0.]))
self.assertTrue(np.sum(np.abs(x - nx)) > 1e-1)
# complex reset
x = env.reset(offset=250, egoid=3194, start=8886)
nx = env.reset(offset=250, egoid=3194, start=8886)
np.testing.assert_array_almost_equal(x, nx, 4)
def perf_vectorized_ngsim_env_step():
basedir = os.path.expanduser('~/.julia/packages/NGSIM/9OYUa/data')
filename = 'trajdata_i101_trajectories-0750am-0805am.txt'
filepaths = [os.path.join(basedir, filename)]
n_envs = 100
env = JuliaEnv(env_id='VectorizedNGSIMEnv',
env_params=dict(
n_envs=n_envs,
trajectory_filepaths=filepaths,
),
using='AutoEnvs')
n_steps = 10000
action = np.zeros((n_envs, 2))
action[:, 0] = 1.
env.reset()
with ContextTimer():
for _ in range(0, n_steps, n_envs):
_, _, terminal, _ = env.step(action)
env.reset(terminal)