def __init__(
self,
env_spec,
hidden_sizes=(),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
dist_cls=DiagonalGaussian,
hlc_output_dim=0,
subnet_split1=[2, 3, 4, 11, 12, 13],
subnet_split2=[5, 6, 7, 14, 15, 16],
sub_out_dim=3,
option_dim=4,
):
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = HMLPPhase(
hidden_sizes,
hidden_nonlinearity,
input_shape=(obs_dim, ),
subnet_split1=subnet_split1,
subnet_split2=subnet_split2,
hlc_output_dim=hlc_output_dim,
sub_out_dim=sub_out_dim,
option_dim=option_dim,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self.min_std = min_std
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
self.hidden_signals = ext.compile_function(
inputs=[obs_var],
outputs=[
mean_network.hlc_signal1, mean_network.hlc_signal2,
mean_network.leg1_part, mean_network.leg2_part
])
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
dist_cls=DiagonalGaussian,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Discrete)
#obs_dim = env_spec.observation_space.flat_dim
obs_dim = 6400
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = MLP(
input_shape=(obs_dim,),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim,),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self.min_std = min_std
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
input_shape,
output_dim,
mean_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
optimizer=None,
use_trust_region=True,
step_size=0.01,
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_nonlinearity=None,
normalize_inputs=True,
normalize_outputs=True,
name=None,
):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean network.
:param hidden_nonlinearity: Non-linearity used for each layer of the mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
:param use_trust_region: Whether to use trust region constraint.
:param step_size: KL divergence constraint for each iteration
:param learn_std: Whether to learn the standard deviations. Only effective if adaptive_std is False. If
adaptive_std is True, this parameter is ignored, and the weights for the std network are always learned.
:param adaptive_std: Whether to make the std a function of the states.
:param std_share_network: Whether to use the same network as the mean.
:param std_hidden_sizes: Number of hidden units of each layer of the std network. Only used if
`std_share_network` is False. It defaults to the same architecture as the mean.
:param std_nonlinearity: Non-linearity used for each layer of the std network. Only used if `std_share_network`
is False. It defaults to the same non-linearity as the mean.
"""
Serializable.quick_init(self, locals())
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer()
else:
optimizer = LbfgsOptimizer()
self._optimizer = optimizer
if mean_network is None:
mean_network = MLP(
input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=None,
)
l_mean = mean_network.output_layer
if adaptive_std:
l_log_std = MLP(
input_shape=input_shape,
input_var=mean_network.input_layer.input_var,
output_dim=output_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_nonlinearity,
output_nonlinearity=None,
).output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=output_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
LasagnePowered.__init__(self, [l_mean, l_log_std])
xs_var = mean_network.input_layer.input_var
ys_var = TT.matrix("ys")
old_means_var = TT.matrix("old_means")
old_log_stds_var = TT.matrix("old_log_stds")
x_mean_var = theano.shared(np.zeros((1, ) + input_shape),
name="x_mean",
broadcastable=(True, ) +
(False, ) * len(input_shape))
x_std_var = theano.shared(np.ones((1, ) + input_shape),
name="x_std",
broadcastable=(True, ) +
(False, ) * len(input_shape))
y_mean_var = theano.shared(np.zeros((1, output_dim)),
name="y_mean",
broadcastable=(True, False))
y_std_var = theano.shared(np.ones((1, output_dim)),
name="y_std",
broadcastable=(True, False))
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
normalized_ys_var = (ys_var - y_mean_var) / y_std_var
normalized_means_var = L.get_output(
l_mean, {mean_network.input_layer: normalized_xs_var})
normalized_log_stds_var = L.get_output(
l_log_std, {mean_network.input_layer: normalized_xs_var})
means_var = normalized_means_var * y_std_var + y_mean_var
log_stds_var = normalized_log_stds_var + TT.log(y_std_var)
normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var
normalized_old_log_stds_var = old_log_stds_var - TT.log(y_std_var)
dist = self._dist = DiagonalGaussian()
normalized_dist_info_vars = dict(mean=normalized_means_var,
log_std=normalized_log_stds_var)
mean_kl = TT.mean(
dist.kl_sym(
dict(mean=normalized_old_means_var,
log_std=normalized_old_log_stds_var),
normalized_dist_info_vars,
))
loss = -TT.mean(
dist.log_likelihood_sym(normalized_ys_var,
normalized_dist_info_vars))
self._f_predict = compile_function([xs_var], means_var)
self._f_pdists = compile_function([xs_var], [means_var, log_stds_var])
self._l_mean = l_mean
self._l_log_std = l_log_std
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[normalized_means_var, normalized_log_stds_var],
)
if use_trust_region:
optimizer_args["leq_constraint"] = (mean_kl, step_size)
optimizer_args["inputs"] = [
xs_var, ys_var, old_means_var, old_log_stds_var
]
else:
optimizer_args["inputs"] = [xs_var, ys_var]
self._optimizer.update_opt(**optimizer_args)
self._use_trust_region = use_trust_region
self._name = name
self._normalize_inputs = normalize_inputs
self._normalize_outputs = normalize_outputs
self._x_mean_var = x_mean_var
self._x_std_var = x_std_var
self._y_mean_var = y_mean_var
self._y_std_var = y_std_var
def run_task(vv, log_dir=None, exp_name=None):
global policy
global baseline
trpo_stepsize = 0.01
trpo_subsample_factor = 0.2
# Check if variant is available
if vv['model_type'] not in ['BrushTireModel', 'LinearTireModel']:
raise ValueError('Unrecognized model type for simulating robot')
if vv['robot_type'] not in ['MRZR', 'RCCar']:
raise ValueError('Unrecognized robot type')
# Load environment
if not vv['use_ros']:
env = CircleEnv(target_velocity=vv['target_velocity'],
radius=vv['radius'],
dt=vv['dt'],
model_type=vv['model_type'],
robot_type=vv['robot_type'])
else:
from aa_simulation.envs.circle.circle_env_ros import CircleEnvROS
env = CircleEnvROS(target_velocity=vv['target_velocity'],
radius=vv['radius'],
dt=vv['dt'],
model_type=vv['model_type'],
robot_type=vv['robot_type'])
# Save variant information for comparison plots
variant_file = logger.get_snapshot_dir() + '/variant.json'
logger.log_variant(variant_file, vv)
# Set variance for each action component separately for exploration
# Note: We set the variance manually because we are not scaling our
# action space during training.
init_std_speed = vv['target_velocity'] / 4
init_std_steer = np.pi / 6
init_std = [init_std_speed, init_std_steer]
# Build policy and baseline networks
# Note: Mean of policy network set to analytically computed values for
# faster training (rough estimates for RL to fine-tune).
if policy is None or baseline is None:
wheelbase = 0.257
target_velocity = vv['target_velocity']
target_steering = np.arctan(wheelbase / vv['radius']) # CCW
output_mean = np.array([target_velocity, target_steering])
hidden_sizes = (32, 32)
# In mean network, allow output b values to dominate final output
# value by constraining the magnitude of the output W matrix. This is
# to allow faster learning. These numbers are arbitrarily chosen.
W_gain = min(vv['target_velocity'] / 5, np.pi / 15)
mean_network = MLP(input_shape=(env.spec.observation_space.flat_dim, ),
output_dim=env.spec.action_space.flat_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=LN.tanh,
output_nonlinearity=None,
output_W_init=LI.GlorotUniform(gain=W_gain),
output_b_init=output_mean)
policy = GaussianMLPPolicy(env_spec=env.spec,
hidden_sizes=hidden_sizes,
init_std=init_std,
mean_network=mean_network)
baseline = LinearFeatureBaseline(env_spec=env.spec,
target_key='returns')
# Reset variance to re-enable exploration when using pre-trained networks
else:
policy._l_log_std = ParamLayer(
policy._mean_network.input_layer,
num_units=env.spec.action_space.flat_dim,
param=LI.Constant(np.log(init_std)),
name='output_log_std',
trainable=True)
obs_var = policy._mean_network.input_layer.input_var
mean_var, log_std_var = L.get_output(
[policy._l_mean, policy._l_log_std])
policy._log_std_var = log_std_var
LasagnePowered.__init__(policy, [policy._l_mean, policy._l_log_std])
policy._f_dist = ext.compile_function(inputs=[obs_var],
outputs=[mean_var, log_std_var])
safety_baseline = LinearFeatureBaseline(env_spec=env.spec,
target_key='safety_returns')
safety_constraint = CircleSafetyConstraint(max_value=1.0,
eps=vv['eps'],
baseline=safety_baseline)
if vv['algo'] == 'TRPO':
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=600,
max_path_length=env.horizon,
n_itr=600,
discount=0.99,
step_size=trpo_stepsize,
plot=False,
)
else:
algo = CPO(env=env,
policy=policy,
baseline=baseline,
safety_constraint=safety_constraint,
batch_size=600,
max_path_length=env.horizon,
n_itr=600,
discount=0.99,
step_size=trpo_stepsize,
gae_lambda=0.95,
safety_gae_lambda=1,
optimizer_args={'subsample_factor': trpo_subsample_factor},
plot=False)
algo.train()
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
split_masks=None,
dist_cls=DiagonalGaussian,
mp_dim=0,
mp_sel_hid_dim=0,
mp_sel_num=0,
mp_projection_dim=2,
net_mode=0, # 0: vanilla, 1: append mp to second layer, 2: project mp to lower space, 3: mp selection blending, 4: mp selection discrete
split_init_net=None,
split_units=None,
wc_net_path=None,
learn_segment=False,
split_num=1,
split_layer=[0],
split_std=False,
task_id=0,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
if net_mode == 1:
mean_network = MLPAppend(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
append_dim=mp_dim,
)
elif net_mode == 2:
mean_network = MLP_PROJ(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
mp_dim=mp_dim,
mp_hid_dim=16,
mp_proj_dim=mp_projection_dim,
)
elif net_mode == 3:
mean_network = MLP_PS(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
mp_dim=mp_dim,
mp_sel_hid_dim=mp_sel_hid_dim,
mp_sel_num=mp_sel_num,
)
elif net_mode == 4:
wc_net = joblib.load(wc_net_path)
mean_network = MLP_PSD(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
mp_dim=mp_dim,
mp_sel_hid_dim=mp_sel_hid_dim,
mp_sel_num=mp_sel_num,
wc_net=wc_net,
learn_segment=learn_segment,
)
elif net_mode == 5:
mean_network = MLP_Split(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
split_layer=split_layer,
split_num=split_num,
)
elif net_mode == 6:
mean_network = MLP_SplitAct(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
split_num=split_num,
split_units=split_units,
init_net=split_init_net._mean_network,
)
elif net_mode == 7:
mean_network = MLP_SoftSplit(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
split_num=split_num,
init_net=split_init_net._mean_network,
)
elif net_mode == 8:
mean_network = MLP_MaskedSplit(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
split_num=split_num,
split_masks=split_masks,
init_net=split_init_net._mean_network,
)
elif net_mode == 9:
mean_network = MLP_MaskedSplitCont(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
task_id=task_id,
init_net=split_init_net._mean_network,
)
else:
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
else:
if net_mode != 8 or not split_std:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
else:
l_log_std = ParamLayerSplit(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
split_num=split_num,
init_param=split_init_net.get_params()[-1])
if net_mode == 6 or net_mode == 7 or (net_mode == 8
and not split_std):
l_log_std.get_params()[0].set_value(
split_init_net.get_params()[-1].get_value())
if net_mode == 9:
l_log_std.get_params()[0].set_value(
split_init_net.get_params()[-1].get_value() + 0.5)
self.min_std = min_std
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
if net_mode == 3 or net_mode == 4:
self._f_blendweight = ext.compile_function(
inputs=[obs_var], outputs=[self._mean_network._blend_weights])
entropy = -TT.mean(self._mean_network._blend_weights *
TT.log(self._mean_network._blend_weights))
self._f_weightentropy = ext.compile_function(inputs=[obs_var],
outputs=[entropy])
avg_weights = TT.mean(self._mean_network._blend_weights, axis=0)
entropy2 = -TT.mean(avg_weights * TT.log(avg_weights))
self._f_choiceentropy = ext.compile_function(inputs=[obs_var],
outputs=[entropy2])
def __init__(
self,
env_spec,
env,
latent_dim=2,
latent_name='bernoulli',
bilinear_integration=False,
resample=False,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
min_std=1e-4,
pkl_path=None,
):
"""
:param latent_dim: dimension of the latent variables
:param latent_name: distribution of the latent variables
:param bilinear_integration: Boolean indicator of bilinear integration or simple concatenation
:param resample: Boolean indicator of resampling at every step or only at the start of the rollout (or whenever
agent is reset, which can happen several times along the rollout with rollout in utils_snn)
"""
self.latent_dim = latent_dim ##could I avoid needing this self for the get_action?
self.latent_name = latent_name
self.bilinear_integration = bilinear_integration
self.resample = resample
self.min_std = min_std
self.hidden_sizes = hidden_sizes
self.pre_fix_latent = np.array(
[]
) # if this is not empty when using reset() it will use this latent
self.latent_fix = np.array(
[]) # this will hold the latents variable sampled in reset()
self._set_std_to_0 = False
self.pkl_path = pkl_path
if self.pkl_path:
data = joblib.load(os.path.join(config.PROJECT_PATH,
self.pkl_path))
self.old_policy = data["policy"]
self.latent_dim = self.old_policy.latent_dim
self.latent_name = self.old_policy.latent_name
self.bilinear_integration = self.old_policy.bilinear_integration
self.resample = self.old_policy.resample # this could not be needed...
self.min_std = self.old_policy.min_std
self.hidden_sizes_snn = self.old_policy.hidden_sizes
if latent_name == 'normal':
self.latent_dist = DiagonalGaussian(self.latent_dim)
self.latent_dist_info = dict(mean=np.zeros(self.latent_dim),
log_std=np.zeros(self.latent_dim))
elif latent_name == 'bernoulli':
self.latent_dist = Bernoulli(self.latent_dim)
self.latent_dist_info = dict(p=0.5 * np.ones(self.latent_dim))
elif latent_name == 'categorical':
self.latent_dist = Categorical(self.latent_dim)
if self.latent_dim > 0:
self.latent_dist_info = dict(prob=1. / self.latent_dim *
np.ones(self.latent_dim))
else:
self.latent_dist_info = dict(prob=np.ones(self.latent_dim))
else:
raise NotImplementedError
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
# retrieve dimensions from env!
if isinstance(env, MazeEnv) or isinstance(env, GatherEnv):
self.obs_robot_dim = env.robot_observation_space.flat_dim
self.obs_maze_dim = env.maze_observation_space.flat_dim
elif isinstance(env, NormalizedEnv):
if isinstance(env.wrapped_env, MazeEnv) or isinstance(
env.wrapped_env, GatherEnv):
self.obs_robot_dim = env.wrapped_env.robot_observation_space.flat_dim
self.obs_maze_dim = env.wrapped_env.maze_observation_space.flat_dim
else:
self.obs_robot_dim = env.wrapped_env.observation_space.flat_dim
self.obs_maze_dim = 0
else:
self.obs_robot_dim = env.observation_space.flat_dim
self.obs_maze_dim = 0
# print("the dims of the env are(rob/maze): ", self.obs_robot_dim, self.obs_maze_dim)
all_obs_dim = env_spec.observation_space.flat_dim
assert all_obs_dim == self.obs_robot_dim + self.obs_maze_dim
if self.bilinear_integration:
obs_dim = self.obs_robot_dim + self.latent_dim +\
self.obs_robot_dim * self.latent_dim
else:
obs_dim = self.obs_robot_dim + self.latent_dim # here only if concat.
action_dim = env_spec.action_space.flat_dim
# for _ in range(10):
# print("OK!")
# print(obs_dim)
# print(env_spec.observation_space.flat_dim)
# print(self.latent_dim)
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
name="meanMLP",
)
self._layers_mean = mean_network.layers
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if adaptive_std:
log_std_network = MLP(input_shape=(obs_dim, ),
input_var=obs_var,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
name="log_stdMLP")
l_log_std = log_std_network.output_layer
self._layers_log_std = log_std_network.layers
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self._layers_log_std = [l_log_std]
self._layers_snn = self._layers_mean + self._layers_log_std # this returns a list with the "snn" layers
if self.pkl_path: # restore from pkl file
data = joblib.load(os.path.join(config.PROJECT_PATH,
self.pkl_path))
warm_params = data['policy'].get_params_internal()
self.set_params_snn(warm_params)
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(self.min_std))
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = DiagonalGaussian(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy_snn_restorable, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
env_spec,
zero_gradient_cutoff,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
dist_cls=DiagonalGaussian,
adversarial=True,
eps=0.1,
probability=0.0,
use_dynamics=False,
random=False,
observable_noise=False,
use_max_norm=True,
record_traj=False,
set_dynamics=None,
mask_augmentation=False,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param dist_cls: defines probability distribution over actions
The following parameters are specific to the AdversarialPolicy Class.
:param adversarial: whether the policy should incorporate adversarial states during rollout
:param eps: the strength of the adversarial perturbation
:param probability: frequency of adversarial updates. If 0, do exactly one update at the beginning of
every episode
:param use_dynamics: if True, generate adversarial dynamics updates, otherwise do adversarial state updates
:param random: if True, use a random perturbation instead of an adversarial perturbation
:param observable_noise: if True, don't set adversarial state in the environment, treat it as noise
on observation
:param zero_gradient_cutoff: determines cutoff index for zero-ing out gradients - this is useful when doing
adversarial dynamics vs. adversarial states, when we only want to compute
gradients for one section of the augmented state vector. We also use this to
determine what the original, non-augmented state size is.
:param use_max_norm: if True, use Fast Gradient Sign Method (FGSM) to generate adversarial perturbations, else
use full gradient ascent
:param record_traj: if True, rollout dictionaries will contain qpos and qvel trajectories. This is useful for
plotting trajectories.
:param set_dynamics: if provided, the next rollout initializes the environment to the passed dynamics.
:param mask_augmentation: if True, don't augment the state (even though the environment augments the state with
the dynamics parameters, the policy will ignore these dimensions)
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# TODO: make a more elegant solution to this
# This is here because we assume the original, unaugmented state size is provided.
assert (zero_gradient_cutoff is not None)
# if we're ignoring state augmentation, modify observation size / network size accordingly
if mask_augmentation:
obs_dim = zero_gradient_cutoff
# create network
if mean_network is None:
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self.min_std = min_std
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_log_std = l_log_std
# take exponential for the actual standard dev
self._tru_std_var = TT.exp(self._log_std_var)
# take gradients of mean network, exponential of std network wrt L2 norm
self._mean_grad = theano.grad(self._mean_var.norm(2), obs_var)
self._std_grad = theano.grad(self._tru_std_var.norm(2),
obs_var,
disconnected_inputs='warn')
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(AdversarialPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
# function to get gradients
self._f_grad_dist = ext.compile_function(
inputs=[obs_var], outputs=[self._mean_grad, self._std_grad])
# initialize adversarial parameters
self.adversarial = adversarial
self.eps = eps
self.probability = probability
self.use_dynamics = use_dynamics
self.random = random
self.observable_noise = observable_noise
self.zero_gradient_cutoff = zero_gradient_cutoff
self.use_max_norm = use_max_norm
self.record_traj = record_traj
self.set_dynamics = set_dynamics
self.mask_augmentation = mask_augmentation
def __init__(
self,
env_spec,
env,
pkl_path=None,
json_path=None,
npz_path=None,
trainable_snn=True,
##CF - latents units at the input
latent_dim=3, # we keep all these as the dim of the output of the other MLP and others that we will need!
latent_name='categorical',
bilinear_integration=False, # again, needs to match!
resample=False, # this can change: frequency of resampling the latent?
hidden_sizes_snn=(32, 32),
hidden_sizes_selector=(10, 10),
external_latent=False,
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
min_std=1e-4,
):
self.latent_dim = latent_dim ## could I avoid needing this self for the get_action?
self.latent_name = latent_name
self.bilinear_integration = bilinear_integration
self.resample = resample
self.min_std = min_std
self.hidden_sizes_snn = hidden_sizes_snn
self.hidden_sizes_selector = hidden_sizes_selector
self.pre_fix_latent = np.array([]) # if this is not empty when using reset() it will use this latent
self.latent_fix = np.array([]) # this will hold the latents variable sampled in reset()
self.shared_latent_var = theano.shared(self.latent_fix) # this is for external lat! update that
self._set_std_to_0 = False
self.trainable_snn = trainable_snn
self.external_latent = external_latent
self.pkl_path = pkl_path
self.json_path = json_path
self.npz_path = npz_path
self.old_policy = None
if self.json_path: # there is another one after defining all the NN to warm-start the params of the SNN
data = json.load(
open(os.path.join(config.PROJECT_PATH, self.json_path), 'r')) # I should do this with the json file
self.old_policy_json = data['json_args']["policy"]
self.latent_dim = self.old_policy_json['latent_dim']
self.latent_name = self.old_policy_json['latent_name']
self.bilinear_integration = self.old_policy_json['bilinear_integration']
self.resample = self.old_policy_json['resample'] # this could not be needed...
self.min_std = self.old_policy_json['min_std']
self.hidden_sizes_snn = self.old_policy_json['hidden_sizes']
elif self.pkl_path:
data = joblib.load(os.path.join(config.PROJECT_PATH, self.pkl_path))
self.old_policy = data["policy"]
self.latent_dim = self.old_policy.latent_dim
self.latent_name = self.old_policy.latent_name
self.bilinear_integration = self.old_policy.bilinear_integration
self.resample = self.old_policy.resample # this could not be needed...
self.min_std = self.old_policy.min_std
self.hidden_sizes_snn = self.old_policy.hidden_sizes
if self.latent_name == 'normal':
self.latent_dist = DiagonalGaussian(self.latent_dim)
self.latent_dist_info = dict(mean=np.zeros(self.latent_dim), log_std=np.zeros(self.latent_dim))
elif self.latent_name == 'bernoulli':
self.latent_dist = Bernoulli(self.latent_dim)
self.latent_dist_info = dict(p=0.5 * np.ones(self.latent_dim))
elif self.latent_name == 'categorical':
self.latent_dist = Categorical(self.latent_dim)
if self.latent_dim > 0:
self.latent_dist_info = dict(prob=1. / self.latent_dim * np.ones(self.latent_dim))
else:
self.latent_dist_info = dict(prob=np.ones(self.latent_dim)) # this is an empty array
else:
raise NotImplementedError
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
# retrieve dimensions and check consistency
if isinstance(env, MazeEnv) or isinstance(env, GatherEnv):
self.obs_robot_dim = env.robot_observation_space.flat_dim
self.obs_maze_dim = env.maze_observation_space.flat_dim
elif isinstance(env, NormalizedEnv):
if isinstance(env.wrapped_env, MazeEnv) or isinstance(env.wrapped_env, GatherEnv):
self.obs_robot_dim = env.wrapped_env.robot_observation_space.flat_dim
self.obs_maze_dim = env.wrapped_env.maze_observation_space.flat_dim
else:
self.obs_robot_dim = env.wrapped_env.observation_space.flat_dim
self.obs_maze_dim = 0
else:
self.obs_robot_dim = env.observation_space.flat_dim
self.obs_maze_dim = 0
# print("the dims of the env are(rob/maze): ", self.obs_robot_dim, self.obs_maze_dim)
all_obs_dim = env_spec.observation_space.flat_dim
assert all_obs_dim == self.obs_robot_dim + self.obs_maze_dim
if self.external_latent: # in case we want to fix the latent externally
l_all_obs_var = L.InputLayer(shape=(None,) + (self.obs_robot_dim + self.obs_maze_dim,))
all_obs_var = l_all_obs_var.input_var
# l_selection = ConstOutputLayer(incoming=l_all_obs_var, output_var=self.shared_latent_var)
l_selection = ParamLayer(incoming=l_all_obs_var, num_units=self.latent_dim, param=self.shared_latent_var,
trainable=False) # Rui: change False to True? this is a simple layer that directly outputs self.shared_latent_var
selection_var = L.get_output(l_selection)
else:
# create network with softmax output: it will be the latent 'selector'!
latent_selection_network = MLP(
input_shape=(self.obs_robot_dim + self.obs_maze_dim,),
output_dim=self.latent_dim,
hidden_sizes=self.hidden_sizes_selector,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=NL.softmax,
)
l_all_obs_var = latent_selection_network.input_layer
all_obs_var = latent_selection_network.input_layer.input_var
# collect the output to select the behavior of the robot controller (equivalent to latents)
l_selection = latent_selection_network.output_layer
selection_var = L.get_output(l_selection)
# split all_obs into the robot and the maze obs --> ROBOT goes first!!
l_obs_robot = CropLayer(l_all_obs_var, start_index=None, end_index=self.obs_robot_dim)
l_obs_maze = CropLayer(l_all_obs_var, start_index=self.obs_robot_dim, end_index=None)
# for _ in range(10):
# print("OK!")
# print(self.obs_robot_dim)
# print(self.obs_maze_dim)
obs_robot_var = all_obs_var[:, :self.obs_robot_dim]
obs_maze_var = all_obs_var[:, self.obs_robot_dim:]
# Enlarge obs with the selectors (or latents). Here just computing the final input dim
if self.bilinear_integration:
l_obs_snn = BilinearIntegrationLayer([l_obs_robot, l_selection])
else:
l_obs_snn = L.ConcatLayer([l_obs_robot, l_selection])
action_dim = env_spec.action_space.flat_dim
# create the action network
mean_network = MLP(
input_layer=l_obs_snn, # input the layer that handles the integration of the selector
output_dim=action_dim,
hidden_sizes=self.hidden_sizes_snn,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
name="meanMLP",
)
self._layers_mean = mean_network.layers
l_mean = mean_network.output_layer
if adaptive_std:
log_std_network = MLP(
input_layer=l_obs_snn,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
name="log_stdMLP"
)
l_log_std = log_std_network.output_layer
self._layers_log_std = log_std_network.layers
else:
l_log_std = ParamLayer(
incoming=mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self._layers_log_std = [l_log_std]
self._layers_snn = self._layers_mean + self._layers_log_std # this returns a list with the "snn" layers
if not self.trainable_snn:
for layer in self._layers_snn:
for param, tags in layer.params.items(): # params of layer are OrDict: key=the shared var, val=tags
tags.remove("trainable")
if self.json_path and self.npz_path:
warm_params_dict = dict(np.load(os.path.join(config.PROJECT_PATH, self.npz_path)))
# keys = list(param_dict.keys())
self.set_params_snn(warm_params_dict)
elif self.pkl_path:
data = joblib.load(os.path.join(config.PROJECT_PATH, self.pkl_path))
warm_params = data['policy'].get_params_internal()
self.set_params_snn(warm_params)
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(self.min_std))
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = DiagonalGaussian(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy_snn_hier, self).__init__(env_spec)
# debug
obs_snn_var = L.get_output(l_obs_snn)
self._l_obs_snn = ext.compile_function(
inputs=[all_obs_var],
outputs=obs_snn_var,
)
# self._log_std = ext.compile_function(
# inputs=[all_obs_var],
# outputs=log_std_var,
# )
self._mean = ext.compile_function(
inputs=[all_obs_var],
outputs=mean_var,
)
self._f_dist = ext.compile_function(
inputs=[all_obs_var],
outputs=[mean_var, log_std_var],
)
# if I want to monitor the selector output
self._f_select = ext.compile_function(
inputs=[all_obs_var],
outputs=selection_var,
)
def __init__(
self,
env_spec,
hidden_sizes=(32, ),
state_include_action=True,
hidden_nonlinearity=NL.tanh,
learn_std=True,
init_std=1.0,
output_nonlinearity=None,
):
"""
:param env_spec: A spec for the env.
:param hidden_sizes: list of sizes for the fully connected hidden layers
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:return:
"""
Serializable.quick_init(self, locals())
super(GaussianGRUPolicy, self).__init__(env_spec)
assert len(hidden_sizes) == 1
if state_include_action:
obs_dim = env_spec.observation_space.flat_dim + env_spec.action_space.flat_dim
else:
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
mean_network = GRUNetwork(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_dim=hidden_sizes[0],
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
l_mean = mean_network.output_layer
obs_var = mean_network.input_var
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
l_step_log_std = ParamLayer(
mean_network.step_input_layer,
num_units=action_dim,
param=l_log_std.param,
name="step_output_log_std",
trainable=learn_std,
)
self._mean_network = mean_network
self._l_log_std = l_log_std
self._state_include_action = state_include_action
self._f_step_mean_std = ext.compile_function(
[
mean_network.step_input_layer.input_var,
mean_network.step_prev_hidden_layer.input_var
],
L.get_output([
mean_network.step_output_layer, l_step_log_std,
mean_network.step_hidden_layer
]))
self._prev_action = None
self._prev_hidden = None
self._hidden_sizes = hidden_sizes
self._dist = RecurrentDiagonalGaussian(action_dim)
self.reset()
self.set_greedy(False)
LasagnePowered.__init__(self, [mean_network.output_layer, l_log_std])
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
dist_cls=DiagonalGaussian,
aux_pred_step=3,
aux_pred_dim=4,
skip_last=-1,
copy_output=False,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
self._aux_pred_network = MLPAux(
aux_pred_step,
aux_pred_dim,
None,
mean_network,
skip_last=skip_last,
copy_output=copy_output,
)
# compile training function
aux_target_var = TT.matrix('aux_targets')
prediction = self._aux_pred_network._output
loss = lasagne.objectives.squared_error(prediction, aux_target_var)
loss = loss.mean()
params = self._aux_pred_network.get_params(trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=0.001)
self.aux_train_fn = T.function(
[self._aux_pred_network.input_layer.input_var, aux_target_var],
loss,
updates=updates)
self.aux_loss = T.function(
[self._aux_pred_network.input_layer.input_var, aux_target_var],
loss)
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self.min_std = min_std
mean_var, log_std_var, aux_pred_var = L.get_output(
[l_mean, l_log_std, self._aux_pred_network.output_layer])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var, self._aux_pred_var = mean_var, log_std_var, aux_pred_var
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(
self, [l_mean, l_log_std, self._aux_pred_network.output_layer])
super(GaussianMLPAuxPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
self._f_auxpred = ext.compile_function(
inputs=[self._aux_pred_network.input_layer.input_var],
outputs=[prediction],
)
def __init__(
self,
env_spec,
env,
pkl_paths=(),
json_paths=(),
npz_paths=(),
trainable_old=True,
external_selector=False,
hidden_sizes_selector=(10, 10),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
min_std=1e-4,
):
"""
:param pkl_paths: tuple/list of pkl paths
:param json_paths: tuple/list of json paths
:param npz_paths: tuple/list of npz paths
:param trainable_old: Are the old policies still trainable
:param external_selector: is the linear combination of the old policies outputs fixed externally
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
"""
# define where are the old policies to use and what to do with them:
self.trainable_old = trainable_old # whether to keep training the old policies loaded here
self.pkl_paths = pkl_paths
self.json_paths = json_paths
self.npz_paths = npz_paths
self.selector_dim = max(
len(json_paths), len(pkl_paths)) # pkl could be zero if giving npz
# if not use a selector NN here, just externally fixed selector variable:
self.external_selector = external_selector # whether to use the selectorNN defined here or the pre_fix_selector
self.pre_fix_selector = np.zeros(
(self.selector_dim)
) # if this is not empty when using reset() it will use this selector
self.selector_fix = np.zeros(
(self.selector_dim
)) # this will hold the selectors variable sampled in reset()
self.shared_selector_var = theano.shared(
self.selector_fix) # this is for external selector! update that
# else, describe the MLP used:
self.hidden_sizes_selector = hidden_sizes_selector # size of the selector NN defined here
self.min_std = min_std
self._set_std_to_0 = False
self.action_dim = env_spec.action_space.flat_dim # not checking that all the old policies have this act_dim
self.old_hidden_sizes = []
# assume json always given
for json_path in self.json_paths:
data = json.load(
open(os.path.join(config.PROJECT_PATH, json_path), 'r'))
old_json_policy = data['json_args']["policy"]
self.old_hidden_sizes.append(old_json_policy['hidden_sizes'])
# retrieve dimensions and check consistency
if isinstance(env, MazeEnv) or isinstance(env, GatherEnv):
self.obs_robot_dim = env.robot_observation_space.flat_dim
self.obs_maze_dim = env.maze_observation_space.flat_dim
elif isinstance(env, NormalizedEnv):
if isinstance(env.wrapped_env, MazeEnv) or isinstance(
env.wrapped_env, GatherEnv):
self.obs_robot_dim = env.wrapped_env.robot_observation_space.flat_dim
self.obs_maze_dim = env.wrapped_env.maze_observation_space.flat_dim
else:
self.obs_robot_dim = env.wrapped_env.observation_space.flat_dim
self.obs_maze_dim = 0
else:
self.obs_robot_dim = env.observation_space.flat_dim
self.obs_maze_dim = 0
# print("the dims of the env are(rob/maze): ", self.obs_robot_dim, self.obs_maze_dim)
all_obs_dim = env_spec.observation_space.flat_dim
assert all_obs_dim == self.obs_robot_dim + self.obs_maze_dim
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
if self.external_selector: # in case we want to fix the selector externally
l_all_obs_var = L.InputLayer(
shape=(None, ) + (self.obs_robot_dim + self.obs_maze_dim, ))
all_obs_var = l_all_obs_var.input_var
l_selection = ParamLayer(incoming=l_all_obs_var,
num_units=self.selector_dim,
param=self.shared_selector_var,
trainable=False)
selection_var = L.get_output(l_selection)
else:
# create network with softmax output: it will be the selector!
selector_network = MLP(
input_shape=(self.obs_robot_dim + self.obs_maze_dim, ),
output_dim=self.selector_dim,
hidden_sizes=self.hidden_sizes_selector,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=NL.softmax,
)
l_all_obs_var = selector_network.input_layer
all_obs_var = selector_network.input_layer.input_var
# collect the output to select the behavior of the robot controller (equivalent to selectors)
l_selection = selector_network.output_layer
selection_var = L.get_output(l_selection)
# split all_obs into the robot and the maze obs --> ROBOT goes first!!
l_obs_robot = CropLayer(l_all_obs_var,
start_index=None,
end_index=self.obs_robot_dim)
l_obs_maze = CropLayer(l_all_obs_var,
start_index=self.obs_robot_dim,
end_index=None)
obs_robot_var = all_obs_var[:, :self.obs_robot_dim]
obs_maze_var = all_obs_var[:, self.obs_robot_dim:]
# create the action networks
self.old_l_means = [
] # I do this self in case I wanna access it from reset
self.old_l_log_stds = []
self.old_layers = []
for i in range(self.selector_dim):
mean_network = MLP(
input_layer=l_obs_robot,
output_dim=self.action_dim,
hidden_sizes=self.old_hidden_sizes[i],
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
name="meanMLP{}".format(i),
)
self.old_l_means.append(mean_network.output_layer)
self.old_layers += mean_network.layers
l_log_std = ParamLayer(
incoming=mean_network.input_layer,
num_units=self.action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std{}".format(i),
trainable=learn_std,
)
self.old_l_log_stds.append(l_log_std)
self.old_layers += [l_log_std]
if not self.trainable_old:
for layer in self.old_layers:
for param, tags in layer.params.items(
): # params of layer are OrDict: key=the shared var, val=tags
tags.remove("trainable")
if self.json_paths and self.npz_paths:
old_params_dict = {}
for i, npz_path in enumerate(self.npz_paths):
params_dict = dict(
np.load(os.path.join(config.PROJECT_PATH, npz_path)))
renamed_warm_params_dict = {}
for key in params_dict.keys():
if key == 'output_log_std.param':
old_params_dict['output_log_std{}.param'.format(
i)] = params_dict[key]
elif 'meanMLP_' == key[:8]:
old_params_dict['meanMLP{}_'.format(i) +
key[8:]] = params_dict[key]
else:
old_params_dict['meanMLP{}_'.format(i) +
key] = params_dict[key]
self.set_old_params(old_params_dict)
elif self.pkl_paths:
old_params_dict = {}
for i, pkl_path in enumerate(self.pkl_paths):
data = joblib.load(os.path.join(config.PROJECT_PATH, pkl_path))
params = data['policy'].get_params_internal()
for param in params:
if param.name == 'output_log_std.param':
old_params_dict['output_log_std{}.param'.format(
i)] = param.get_value()
elif 'meanMLP_' == param.name[:8]:
old_params_dict['meanMLP{}_'.format(i) +
param.name[8:]] = param.get_value()
else:
old_params_dict['meanMLP{}_'.format(i) +
param.name] = param.get_value()
self.set_old_params(old_params_dict)
# new layers actually selecting the correct output
l_mean = SumProdLayer(self.old_l_means + [l_selection])
l_log_std = SumProdLayer(self.old_l_log_stds + [l_selection])
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(self.min_std))
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = DiagonalGaussian(self.action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy_multi_hier, self).__init__(env_spec)
self._f_old_means = ext.compile_function(
inputs=[all_obs_var],
outputs=[
L.get_output(l_old_mean) for l_old_mean in self.old_l_means
])
self._f_all_inputs = ext.compile_function(
inputs=[all_obs_var],
outputs=[
L.get_output(l_old_mean) for l_old_mean in self.old_l_means
] + [selection_var])
self._f_dist = ext.compile_function(
inputs=[all_obs_var],
outputs=[mean_var, log_std_var],
)
# if I want to monitor the selector output
self._f_select = ext.compile_function(
inputs=[all_obs_var],
outputs=selection_var,
)
def __init__(
self,
env_spec,
latent_dim=2,
latent_name='bernoulli',
bilinear_integration=False,
resample=False,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
min_std=1e-4,
):
"""
:param latent_dim: dimension of the latent variables
:param latent_name: distribution of the latent variables
:param bilinear_integration: Boolean indicator of bilinear integration or simple concatenation
:param resample: Boolean indicator of resampling at every step or only at the start of the rollout (or whenever
agent is reset, which can happen several times along the rollout with rollout in utils_snn)
"""
self.latent_dim = latent_dim ##could I avoid needing this self for the get_action?
self.latent_name = latent_name
self.bilinear_integration = bilinear_integration
self.resample = resample
self.min_std = min_std
self.hidden_sizes = hidden_sizes
self.pre_fix_latent = np.array(
[]
) # if this is not empty when using reset() it will use this latent
self.latent_fix = np.array(
[]) # this will hold the latents variable sampled in reset()
self._set_std_to_0 = False
if latent_name == 'normal':
self.latent_dist = DiagonalGaussian(self.latent_dim)
self.latent_dist_info = dict(mean=np.zeros(self.latent_dim),
log_std=np.zeros(self.latent_dim))
elif latent_name == 'bernoulli':
self.latent_dist = Bernoulli(self.latent_dim)
self.latent_dist_info = dict(p=0.5 * np.ones(self.latent_dim))
elif latent_name == 'categorical':
self.latent_dist = Categorical(self.latent_dim)
if self.latent_dim > 0:
self.latent_dist_info = dict(prob=1. / self.latent_dim *
np.ones(self.latent_dim))
else:
self.latent_dist_info = dict(prob=np.ones(self.latent_dim))
else:
raise NotImplementedError
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
if self.bilinear_integration:
obs_dim = env_spec.observation_space.flat_dim + latent_dim +\
env_spec.observation_space.flat_dim * latent_dim
else:
obs_dim = env_spec.observation_space.flat_dim + latent_dim # here only if concat.
action_dim = env_spec.action_space.flat_dim
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
name="meanMLP",
)
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if adaptive_std:
l_log_std = MLP(input_shape=(obs_dim, ),
input_var=obs_var,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
name="log_stdMLP").output_layer
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(self.min_std))
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = DiagonalGaussian(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy_snn, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
npz_path=None,
freeze_lst=None,
reinit_lst=None,
std_hidden_nonlinearity=NL.tanh,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
dist_cls=DiagonalGaussian,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:return:
"""
Serializable.quick_init(self, locals())
# reinit_lst = None
assert isinstance(env_spec.action_space, Box)
if init_std is None:
init_std = 1.0
set_std_params = False
else:
set_std_params = True
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
self._layers_mean = mean_network.layers
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_log_std = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_log_std = std_network.output_layer
self._layers_log_std = std_network.layers
else:
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self._layers_log_std = [l_log_std]
self._layers = self._layers_mean + self._layers_log_std
self.min_std = min_std
mean_var, log_std_var = L.get_output([l_mean, l_log_std])
if self.min_std is not None:
log_std_var = TT.maximum(log_std_var, np.log(min_std))
self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_log_std = l_log_std
self._dist = dist_cls(action_dim)
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(GaussianMLPPolicy, self).__init__(env_spec)
self._f_dist = ext.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
if npz_path is not None:
param_dict = dict(
np.load(os.path.join(config.PROJECT_PATH, npz_path)))
param_values = param_dict['params']
# todo: don't forget about this
if set_std_params:
self.set_param_values(param_values)
else:
self.set_param_values_transfer(param_values)
if freeze_lst is not None:
assert len(freeze_lst) == len(self._layers) - 1
for layer, should_freeze in zip(self._layers[1:], freeze_lst):
if should_freeze:
for param, tags in layer.params.items():
tags.remove("trainable")
if reinit_lst is not None:
assert len(freeze_lst) == len(
self._layers) - 1 # since input layer is counted
for layer, should_reinit in zip(self._layers[1:], reinit_lst):
if should_reinit:
print("reinitialized")
for v in layer.params:
val = v.get_value()
if (len(val.shape) < 2):
v.set_value(lasagne.init.Constant(0.0)(val.shape))
else:
v.set_value(lasagne.init.GlorotUniform()(
val.shape))
else:
print("did not reinit")
def __init__(
self,
env_spec,
input_latent_vars=None,
hidden_sizes=(32, 32),
hidden_latent_vars=None,
learn_std=True,
init_std=1.0,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
):
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
mean_network = StochasticMLP(
input_shape=(obs_dim, ),
input_latent_vars=input_latent_vars,
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_latent_vars=hidden_latent_vars,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
l_log_std = ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=lasagne.init.Constant(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
self._mean_network = mean_network
self._n_latent_layers = len(mean_network.latent_layers)
self._l_mean = l_mean
self._l_log_std = l_log_std
LasagnePowered.__init__(self, [l_mean, l_log_std])
super(StochasticGaussianMLPPolicy, self).__init__(env_spec)
outputs = self.dist_info_sym(mean_network.input_var)
latent_keys = sorted(
set(outputs.keys()).difference({"mean", "log_std"}))
extras = get_full_output([self._l_mean, self._l_log_std] +
self._mean_network.latent_layers, )[1]
latent_distributions = [
extras[layer]["distribution"]
for layer in self._mean_network.latent_layers
]
self._latent_keys = latent_keys
self._latent_distributions = latent_distributions
self._dist = DiagonalGaussian(action_dim)
self._f_dist_info = ext.compile_function(
inputs=[obs_var],
outputs=outputs,
)
self._f_dist_info_givens = None
