class TRPOAgentBase(object):
def __init__(self, env):
self.env = env
# if not isinstance(env.observation_space, Box) or \
# not isinstance(env.action_space, Discrete):
# print("Incompatible spaces.")
# exit(-1)
print("Observation Space", env.observation_space)
print("Action Space", env.action_space)
print("Action area, high:%f, low%f" % (env.action_space.high, env.action_space.low))
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1 / 3.0)
self.session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self.end_count = 0
self.paths = []
self.train = True
self.baseline = Baseline()
self.storage = Storage(self, self.env, self.baseline)
self.distribution = DiagonalGaussian(pms.action_shape)
self.net = None
# def init_logger(self):
# head = ["average_episode_std" , "sum steps episode number" "total number of episodes" ,
# "Average sum of rewards per episode" ,
# "KL between old and new distribution" , "Surrogate loss" , "Surrogate loss prev" , "ds" , "entropy" ,
# "mean_advant"]
# self.logger = Logger(head)
def init_network(self):
raise NotImplementedError
def get_samples(self, path_number):
for i in range(path_number):
self.storage.get_single_path()
def get_action(self, obs, *args):
if self.net==None:
raise NameError("network have not been defined")
obs = np.expand_dims(obs, 0)
# action_dist_logstd = np.expand_dims([np.log(pms.std)], 0)
action_dist_means_n, action_dist_stds_n = self.session.run([self.net.action_dist_means_n, self.action_dist_stds_n],
{self.net.obs: obs})
if pms.train_flag:
rnd = np.random.normal(size=action_dist_means_n[0].shape)
action = rnd * action_dist_stds_n[0] + action_dist_means_n[0]
else:
action = action_dist_means_n[0]
# action = np.clip(action, pms.min_a, pms.max_a)
return action, dict(mean=action_dist_means_n[0], log_std=action_dist_stds_n[0])
def train_mini_batch(self, parallel=False, linear_search=True):
# Generating paths.
self.get_samples(pms.paths_number)
paths = self.storage.get_paths() # get_paths
# Computing returns and estimating advantage function.
return self.train_paths(paths, parallel=parallel, linear_search=linear_search)
def train_paths(self, paths, parallel=False, linear_search=True):
start_time = time.time()
sample_data = self.storage.process_paths(paths)
agent_infos = sample_data["agent_infos"]
obs_n = sample_data["observations"]
action_n = sample_data["actions"]
advant_n = sample_data["advantages"]
n_samples = len(obs_n)
inds = np.random.choice(n_samples , int(math.floor(n_samples * pms.subsample_factor)) , replace=False)
# inds = range(n_samples)
obs_n = obs_n[inds]
action_n = action_n[inds]
advant_n = advant_n[inds]
action_dist_means_n = np.array([agent_info["mean"] for agent_info in agent_infos[inds]])
action_dist_logstds_n = np.array([agent_info["log_std"] for agent_info in agent_infos[inds]])
feed = {self.net.obs: obs_n ,
self.net.advant: advant_n ,
self.net.old_dist_means_n: action_dist_means_n ,
self.net.old_dist_logstds_n: action_dist_logstds_n ,
self.net.action_n: action_n
}
episoderewards = np.array([path["rewards"].sum() for path in paths])
thprev = self.gf() # get theta_old
def fisher_vector_product(p):
feed[self.flat_tangent] = p
return self.session.run(self.fvp , feed) + pms.cg_damping * p
g = self.session.run(self.pg , feed_dict=feed)
stepdir = krylov.cg(fisher_vector_product , -g , cg_iters=pms.cg_iters)
shs = 0.5 * stepdir.dot(fisher_vector_product(stepdir)) # theta
# if shs<0, then the nan error would appear
lm = np.sqrt(shs / pms.max_kl)
fullstep = stepdir / lm
neggdotstepdir = -g.dot(stepdir)
def loss(th):
self.sff(th)
return self.session.run(self.losses , feed_dict=feed)
if parallel is True:
theta = linesearch_parallel(loss , thprev , fullstep , neggdotstepdir / lm)
else:
if linear_search:
theta = linesearch(loss , thprev , fullstep , neggdotstepdir / lm)
else:
theta = thprev + fullstep
if math.isnan(theta.mean()):
print shs is None
theta = thprev
stats = {}
stats["sum steps of episodes"] = sample_data["sum_episode_steps"]
stats["Average sum of rewards per episode"] = episoderewards.mean()
stats["Time elapsed"] = "%.2f mins" % ((time.time() - start_time) / 60.0)
return stats , theta , thprev
def learn(self):
raise NotImplementedError
def test(self, model_name):
self.load_model(model_name)
if pms.record_movie:
for i in range(100):
self.storage.get_single_path()
self.env.env.monitor.close()
if pms.upload_to_gym:
gym.upload("log/trpo",algorithm_id='alg_8BgjkAsQRNiWu11xAhS4Hg', api_key='sk_IJhy3b2QkqL3LWzgBXoVA')
else:
for i in range(50):
self.storage.get_single_path()
def save_model(self, model_name):
self.saver.save(self.session, "checkpoint/" + model_name + ".ckpt")
def load_model(self, model_name):
try:
if model_name is not None:
self.saver.restore(self.session, model_name)
else:
self.saver.restore(self.session, tf.train.latest_checkpoint(pms.checkpoint_dir))
except:
print "load model %s fail" % (model_name)
class TRPOAgentContinousSingleProcess(object):
def __init__(self, thread_id):
print "create worker %d" % (thread_id)
self.thread_id = thread_id
self.env = env = Environment(gym.make(pms.environment_name))
# print("Observation Space", env.observation_space)
# print("Action Space", env.action_space)
# print("Action area, high:%f, low%f" % (env.action_space.high, env.action_space.low))
self.end_count = 0
self.paths = []
self.train = True
self.baseline = Baseline()
self.storage = Storage(self, self.env, self.baseline)
self.distribution = DiagonalGaussian(pms.action_shape)
self.session = self.master.session
self.init_network()
def init_network(self):
self.network = NetworkContinous(str(self.thread_id))
if pms.min_std is not None:
log_std_var = tf.maximum(self.network.action_dist_logstds_n,
np.log(pms.min_std))
self.action_dist_stds_n = tf.exp(log_std_var)
self.old_dist_info_vars = dict(mean=self.network.old_dist_means_n,
log_std=self.network.old_dist_logstds_n)
self.new_dist_info_vars = dict(
mean=self.network.action_dist_means_n,
log_std=self.network.action_dist_logstds_n)
self.likehood_action_dist = self.distribution.log_likelihood_sym(
self.network.action_n, self.new_dist_info_vars)
self.ratio_n = self.distribution.likelihood_ratio_sym(
self.network.action_n, self.new_dist_info_vars,
self.old_dist_info_vars)
surr = -tf.reduce_mean(
self.ratio_n * self.network.advant) # Surrogate loss
kl = tf.reduce_mean(
self.distribution.kl_sym(self.old_dist_info_vars,
self.new_dist_info_vars))
ent = self.distribution.entropy(self.old_dist_info_vars)
# ent = tf.reduce_sum(-p_n * tf.log(p_n + eps)) / Nf
self.losses = [surr, kl, ent]
var_list = self.network.var_list
self.gf = GetFlat(self.session, var_list) # get theta from var_list
self.sff = SetFromFlat(self.session,
var_list) # set theta from var_List
# get g
self.pg = flatgrad(surr, var_list)
# get A
# KL divergence where first arg is fixed
# replace old->tf.stop_gradient from previous kl
kl_firstfixed = kl_sym_gradient(self.network.old_dist_means_n,
self.network.old_dist_logstds_n,
self.network.action_dist_means_n,
self.network.action_dist_logstds_n)
grads = tf.gradients(kl, var_list)
self.flat_tangent = tf.placeholder(dtype, shape=[None])
shapes = map(var_shape, var_list)
start = 0
tangents = []
for shape in shapes:
size = np.prod(shape)
param = tf.reshape(self.flat_tangent[start:(start + size)], shape)
tangents.append(param)
start += size
self.gvp = [tf.reduce_sum(g * t) for (g, t) in zip(grads, tangents)]
self.fvp = flatgrad(tf.reduce_sum(self.gvp), var_list) # get kl''*p
# self.load_model()
def get_samples(self, path_number):
for i in range(pms.paths_number):
self.storage.get_single_path()
def get_action(self, obs, *args):
obs = np.expand_dims(obs, 0)
# action_dist_logstd = np.expand_dims([np.log(pms.std)], 0)
if pms.use_std_network:
action_dist_means_n, action_dist_logstds_n = self.session.run(
[self.action_dist_means_n, self.action_dist_logstds_n],
{self.obs: obs})
if pms.train_flag:
rnd = np.random.normal(size=action_dist_means_n[0].shape)
action = rnd * np.exp(
action_dist_logstds_n[0]) + action_dist_means_n[0]
else:
action = action_dist_means_n[0]
# action = np.clip(action, pms.min_a, pms.max_a)
return action, dict(mean=action_dist_means_n[0],
log_std=action_dist_logstds_n[0])
else:
action_dist_logstd = np.expand_dims([np.log(pms.std)], 0)
action_dist_means_n = self.network.get_action_dist_means_n(
self.session, obs)
if pms.train_flag:
rnd = np.random.normal(size=action_dist_means_n[0].shape)
action = rnd * np.exp(
action_dist_logstd[0]) + action_dist_means_n[0]
else:
action = action_dist_means_n[0]
# action = np.clip(action, pms.min_a, pms.max_a)
return action, dict(mean=action_dist_means_n[0],
log_std=action_dist_logstd[0])
def run(self):
self.learn()
def learn(self):
start_time = time.time()
numeptotal = 0
while True:
i = 0
# Generating paths.
# print("Rollout")
self.get_samples(pms.paths_number)
paths = self.storage.get_paths() # get_paths
# Computing returns and estimating advantage function.
sample_data = self.storage.process_paths(paths)
agent_infos = sample_data["agent_infos"]
obs_n = sample_data["observations"]
action_n = sample_data["actions"]
advant_n = sample_data["advantages"]
n_samples = len(obs_n)
inds = np.random.choice(n_samples,
math.floor(n_samples *
pms.subsample_factor),
replace=False)
obs_n = obs_n[inds]
action_n = action_n[inds]
advant_n = advant_n[inds]
action_dist_means_n = np.array(
[agent_info["mean"] for agent_info in agent_infos[inds]])
action_dist_logstds_n = np.array(
[agent_info["log_std"] for agent_info in agent_infos[inds]])
feed = {
self.network.obs: obs_n,
self.network.advant: advant_n,
self.network.old_dist_means_n: action_dist_means_n,
self.network.old_dist_logstds_n: action_dist_logstds_n,
self.network.action_dist_logstds_n: action_dist_logstds_n,
self.network.action_n: action_n
}
episoderewards = np.array(
[path["rewards"].sum() for path in paths])
average_episode_std = np.mean(np.exp(action_dist_logstds_n))
# print "\n********** Iteration %i ************" % i
for iter_num_per_train in range(pms.iter_num_per_train):
# if not self.train:
# print("Episode mean: %f" % episoderewards.mean())
# self.end_count += 1
# if self.end_count > 100:
# break
if self.train:
thprev = self.gf() # get theta_old
def fisher_vector_product(p):
feed[self.flat_tangent] = p
return self.session.run(self.fvp,
feed) + pms.cg_damping * p
g = self.session.run(self.pg, feed_dict=feed)
stepdir = krylov.cg(fisher_vector_product,
g,
cg_iters=pms.cg_iters)
shs = 0.5 * stepdir.dot(
fisher_vector_product(stepdir)) # theta
fullstep = stepdir * np.sqrt(2.0 * pms.max_kl / shs)
neggdotstepdir = -g.dot(stepdir)
def loss(th):
self.sff(th)
return self.session.run(self.losses, feed_dict=feed)
surr_prev, kl_prev, ent_prev = loss(thprev)
mean_advant = np.mean(advant_n)
theta = linesearch(loss, thprev, fullstep, neggdotstepdir)
self.sff(theta)
surrafter, kloldnew, entnew = self.session.run(
self.losses, feed_dict=feed)
stats = {}
numeptotal += len(episoderewards)
stats["average_episode_std"] = average_episode_std
stats["sum steps of episodes"] = sample_data[
"sum_episode_steps"]
stats["Total number of episodes"] = numeptotal
stats[
"Average sum of rewards per episode"] = episoderewards.mean(
)
# stats["Entropy"] = entropy
# exp = explained_variance(np.array(baseline_n), np.array(returns_n))
# stats["Baseline explained"] = exp
stats["Time elapsed"] = "%.2f mins" % (
(time.time() - start_time) / 60.0)
stats["KL between old and new distribution"] = kloldnew
stats["Surrogate loss"] = surrafter
stats["Surrogate loss prev"] = surr_prev
stats["entropy"] = ent_prev
stats["mean_advant"] = mean_advant
log_data = [
average_episode_std,
len(episoderewards), numeptotal,
episoderewards.mean(), kloldnew, surrafter, surr_prev,
surrafter - surr_prev, ent_prev, mean_advant
]
self.master.logger.log_row(log_data)
# for k, v in stats.iteritems():
# print(k + ": " + " " * (40 - len(k)) + str(v))
# # if entropy != entropy:
# # exit(-1)
# # if exp > 0.95:
# # self.train = False
if self.thread_id == 1:
self.master.save_model("iter" + str(i))
print episoderewards.mean()
i += 1
def test(self, model_name):
self.load_model(model_name)
for i in range(50):
self.storage.get_single_path()
def save_model(self, model_name):
self.saver.save(self.session, "checkpoint/" + model_name + ".ckpt")
def load_model(self, model_name):
try:
self.saver.restore(self.session, model_name)
except:
print "load model %s fail" % (model_name)
class TRPOAgent(object):
def __init__(self):
self.env = env = Environment(gym.make(pms.environment_name))
# if not isinstance(env.observation_space, Box) or \
# not isinstance(env.action_space, Discrete):
# print("Incompatible spaces.")
# exit(-1)
print("Observation Space", env.observation_space)
print("Action Space", env.action_space)
print("Action area, high:%f, low%f" %
(env.action_space.high, env.action_space.low))
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1 / 3.0)
self.session = tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options))
self.end_count = 0
self.paths = []
self.train = True
self.baseline = Baseline(self.session)
self.storage = Storage(self, self.env, self.baseline)
self.distribution = DiagonalGaussian(pms.action_shape)
self.init_network()
if pms.train_flag:
self.init_logger()
def init_logger(self):
head = [
"average_episode_std", "sum steps episode number"
"total number of episodes", "Average sum of rewards per episode",
"KL between old and new distribution", "Surrogate loss",
"Surrogate loss prev", "ds", "entropy", "mean_advant"
]
self.logger = Logger(head)
def init_network(self):
self.obs = obs = tf.placeholder(dtype,
shape=[pms.batch_size, pms.obs_shape],
name="obs")
self.action_n = tf.placeholder(
dtype, shape=[pms.batch_size, pms.action_shape], name="action")
self.advant = tf.placeholder(dtype,
shape=[pms.batch_size],
name="advant")
self.old_dist_means_n = tf.placeholder(
dtype,
shape=[pms.batch_size, pms.action_shape],
name="oldaction_dist_means")
self.old_dist_logstds_n = tf.placeholder(
dtype,
shape=[pms.batch_size, pms.action_shape],
name="oldaction_dist_logstds")
# Create mean network.
# self.fp_mean1, weight_fp_mean1, bias_fp_mean1 = linear(self.obs, 32, activation_fn=tf.nn.tanh, name="fp_mean1")
# self.fp_mean2, weight_fp_mean2, bias_fp_mean2 = linear(self.fp_mean1, 32, activation_fn=tf.nn.tanh, name="fp_mean2")
# self.action_dist_means_n, weight_action_dist_means_n, bias_action_dist_means_n = linear(self.fp_mean2, pms.action_shape, name="action_dist_means")
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(3,
forget_bias=0.0,
state_is_tuple=True)
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell,
output_keep_prob=0.5)
rnn = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 3, state_is_tuple=True)
# rnn = tf.nn.rnn_cell.BasicRNNCell(3)
self.initial_state = state = rnn.zero_state(pms.batch_size, tf.float32)
# output , state = tf.nn.dynamic_rnn(rnn, self.obs)
output, state = rnn(self.obs, state)
print
self.action_dist_means_n = (pt.wrap(output).fully_connected(
16,
activation_fn=tf.nn.tanh,
init=tf.random_normal_initializer(stddev=1.0),
bias=False).fully_connected(
16,
activation_fn=tf.nn.tanh,
init=tf.random_normal_initializer(stddev=1.0),
bias=False).fully_connected(
pms.action_shape,
init=tf.random_normal_initializer(stddev=1.0),
bias=False))
self.N = tf.shape(obs)[0]
Nf = tf.cast(self.N, dtype)
# Create std network.
if pms.use_std_network:
self.action_dist_logstds_n = (pt.wrap(self.obs).fully_connected(
16,
activation_fn=tf.nn.tanh,
init=tf.random_normal_initializer(stddev=1.0),
bias=False).fully_connected(
16,
activation_fn=tf.nn.tanh,
init=tf.random_normal_initializer(stddev=1.0),
bias=False).fully_connected(
pms.action_shape,
init=tf.random_normal_initializer(stddev=1.0),
bias=False))
else:
self.action_dist_logstds_n = tf.placeholder(
dtype, shape=[pms.batch_size, pms.action_shape], name="logstd")
if pms.min_std is not None:
log_std_var = tf.maximum(self.action_dist_logstds_n,
np.log(pms.min_std))
self.action_dist_stds_n = tf.exp(log_std_var)
self.old_dist_info_vars = dict(mean=self.old_dist_means_n,
log_std=self.old_dist_logstds_n)
self.new_dist_info_vars = dict(mean=self.action_dist_means_n,
log_std=self.action_dist_logstds_n)
self.likehood_action_dist = self.distribution.log_likelihood_sym(
self.action_n, self.new_dist_info_vars)
self.ratio_n = self.distribution.likelihood_ratio_sym(
self.action_n, self.new_dist_info_vars, self.old_dist_info_vars)
surr = -tf.reduce_mean(self.ratio_n * self.advant) # Surrogate loss
kl = tf.reduce_mean(
self.distribution.kl_sym(self.old_dist_info_vars,
self.new_dist_info_vars))
ent = self.distribution.entropy(self.old_dist_info_vars)
# ent = tf.reduce_sum(-p_n * tf.log(p_n + eps)) / Nf
self.losses = [surr, kl, ent]
var_list = tf.trainable_variables()
self.gf = GetFlat(self.session, var_list) # get theta from var_list
self.sff = SetFromFlat(self.session,
var_list) # set theta from var_List
# get g
self.pg = flatgrad(surr, var_list)
# get A
# KL divergence where first arg is fixed
# replace old->tf.stop_gradient from previous kl
kl_firstfixed = kl_sym_gradient(self.old_dist_means_n,
self.old_dist_logstds_n,
self.action_dist_means_n,
self.action_dist_logstds_n)
grads = tf.gradients(kl, var_list)
self.flat_tangent = tf.placeholder(dtype, shape=[None])
shapes = map(var_shape, var_list)
start = 0
tangents = []
for shape in shapes:
size = np.prod(shape)
param = tf.reshape(self.flat_tangent[start:(start + size)], shape)
tangents.append(param)
start += size
self.gvp = [tf.reduce_sum(g * t) for (g, t) in zip(grads, tangents)]
self.fvp = flatgrad(tf.reduce_sum(self.gvp), var_list) # get kl''*p
self.session.run(tf.initialize_all_variables())
self.saver = tf.train.Saver(max_to_keep=10)
# self.load_model()
def get_samples(self, path_number):
# thread_pool = []
# for i in range(path_number):
# thread_pool.append(Rollout(i , self.storage))
#
# for thread in thread_pool:
# thread.start()
#
# for thread in thread_pool:
# thread.join()
for i in range(pms.paths_number):
self.storage.get_single_path()
def get_action(self, obs, *args):
obs = np.expand_dims(obs, 0)
temp = np.zeros((pms.batch_size, obs.shape[1]))
for i in range(pms.batch_size):
temp[i - 1] = obs[0]
obs = temp
# action_dist_logstd = np.expand_dims([np.log(pms.std)], 0)
if pms.use_std_network:
action_dist_means_n, action_dist_logstds_n = self.session.run(
[self.action_dist_means_n, self.action_dist_logstds_n],
{self.obs: obs})
if pms.train_flag:
rnd = np.random.normal(size=action_dist_means_n[0].shape)
action = rnd * np.exp(
action_dist_logstds_n[0]) + action_dist_means_n[0]
else:
action = action_dist_means_n[0]
# action = np.clip(action, pms.min_a, pms.max_a)
return action, dict(mean=action_dist_means_n[0],
log_std=action_dist_logstds_n[0])
else:
action_dist_logstd = np.expand_dims([np.log(pms.std)], 0)
action_dist_means_n = self.session.run(self.action_dist_means_n,
{self.obs: obs})
if pms.train_flag:
rnd = np.random.normal(size=action_dist_means_n[0].shape)
action = rnd * np.exp(
action_dist_logstd[0]) + action_dist_means_n[0]
else:
action = action_dist_means_n[0]
# action = np.clip(action, pms.min_a, pms.max_a)
return action, dict(mean=action_dist_means_n[0],
log_std=action_dist_logstd[0])
def learn(self):
start_time = time.time()
numeptotal = 0
i = 0
while True:
# Generating paths.
print("Rollout")
self.get_samples(pms.paths_number)
paths = self.storage.get_paths() # get_paths
# Computing returns and estimating advantage function.
sample_data = self.storage.process_paths([paths[0]])
agent_infos = sample_data["agent_infos"]
obs_n = sample_data["observations"]
action_n = sample_data["actions"]
advant_n = sample_data["advantages"]
n_samples = len(obs_n)
inds = np.array(range(0, n_samples))
obs_n = obs_n[inds]
action_n = action_n[inds]
advant_n = advant_n[inds]
action_dist_means_n = np.array(
[agent_info["mean"] for agent_info in agent_infos[inds]])
action_dist_logstds_n = np.array(
[agent_info["log_std"] for agent_info in agent_infos[inds]])
feed = {
self.obs: obs_n,
self.advant: advant_n,
self.old_dist_means_n: action_dist_means_n,
self.old_dist_logstds_n: action_dist_logstds_n,
self.action_dist_logstds_n: action_dist_logstds_n,
self.action_n: action_n
}
episoderewards = np.array(
[path["rewards"].sum() for path in paths])
average_episode_std = np.mean(np.exp(action_dist_logstds_n))
print "\n********** Iteration %i ************" % i
for iter_num_per_train in range(pms.iter_num_per_train):
# if not self.train:
# print("Episode mean: %f" % episoderewards.mean())
# self.end_count += 1
# if self.end_count > 100:
# break
if self.train:
thprev = self.gf() # get theta_old
def fisher_vector_product(p):
feed[self.flat_tangent] = p
return self.session.run(self.fvp,
feed) + pms.cg_damping * p
g = self.session.run(self.pg, feed_dict=feed)
stepdir = krylov.cg(fisher_vector_product,
g,
cg_iters=pms.cg_iters)
shs = 0.5 * stepdir.dot(
fisher_vector_product(stepdir)) # theta
fullstep = stepdir * np.sqrt(2.0 * pms.max_kl / shs)
neggdotstepdir = -g.dot(stepdir)
def loss(th):
self.sff(th)
return self.session.run(self.losses, feed_dict=feed)
surr_prev, kl_prev, ent_prev = loss(thprev)
mean_advant = np.mean(advant_n)
theta = linesearch(loss, thprev, fullstep, neggdotstepdir)
self.sff(theta)
surrafter, kloldnew, entnew = self.session.run(
self.losses, feed_dict=feed)
stats = {}
numeptotal += len(episoderewards)
stats["average_episode_std"] = average_episode_std
stats["sum steps of episodes"] = sample_data[
"sum_episode_steps"]
stats["Total number of episodes"] = numeptotal
stats[
"Average sum of rewards per episode"] = episoderewards.mean(
)
# stats["Entropy"] = entropy
# exp = explained_variance(np.array(baseline_n), np.array(returns_n))
# stats["Baseline explained"] = exp
stats["Time elapsed"] = "%.2f mins" % (
(time.time() - start_time) / 60.0)
stats["KL between old and new distribution"] = kloldnew
stats["Surrogate loss"] = surrafter
stats["Surrogate loss prev"] = surr_prev
stats["entropy"] = ent_prev
stats["mean_advant"] = mean_advant
log_data = [
average_episode_std,
len(episoderewards), numeptotal,
episoderewards.mean(), kloldnew, surrafter, surr_prev,
surrafter - surr_prev, ent_prev, mean_advant
]
self.logger.log_row(log_data)
for k, v in stats.iteritems():
print(k + ": " + " " * (40 - len(k)) + str(v))
# if entropy != entropy:
# exit(-1)
# if exp > 0.95:
# self.train = False
self.save_model("iter" + str(i))
i += 1
def test(self, model_name):
self.load_model(model_name)
if pms.record_movie:
for i in range(100):
self.storage.get_single_path()
self.env.env.monitor.close()
if pms.upload_to_gym:
gym.upload("log/trpo",
algorithm_id='alg_8BgjkAsQRNiWu11xAhS4Hg',
api_key='sk_IJhy3b2QkqL3LWzgBXoVA')
else:
for i in range(50):
self.storage.get_single_path()
def save_model(self, model_name):
self.saver.save(self.session, "checkpoint/" + model_name + ".ckpt")
def load_model(self, model_name):
try:
self.saver.restore(self.session, model_name)
except:
print "load model %s fail" % (model_name)