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GPOMDP_SVRG_WV_ada_verA_reacher_fr_nver_2.py
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GPOMDP_SVRG_WV_ada_verA_reacher_fr_nver_2.py
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from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.box2d.double_pendulum_env import DoublePendulumEnv
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.envs.normalized_env import normalize
import numpy as np
import theano
import theano.tensor as TT
from rllab.sampler import parallel_sampler
from lasagne.updates import sgd
from lasagne.updates import adam
import matplotlib.pyplot as plt
from rllab.envs.gym_env import GymEnv
import pandas as pd
from lasagne.updates import get_or_compute_grads
from lasagne import utils
from collections import OrderedDict
max_sub_iter = 30
def unpack(i_g):
i_g_arr = [np.array(x) for x in i_g]
res = i_g_arr[0].reshape(i_g_arr[0].shape[0]*i_g_arr[0].shape[1])
res = np.concatenate((res,i_g_arr[1]))
res = i_g_arr[2].reshape(i_g_arr[2].shape[0]*i_g_arr[2].shape[1])
res = np.concatenate((res,i_g_arr[3]))
res = np.concatenate((res,i_g_arr[4][0]))
res = np.concatenate((res,i_g_arr[5]))
res = np.concatenate((res,i_g_arr[6]))
return res
def dis_iw(iw):
z=list()
t=1
for y in iw:
z.append(y*t)
t*=discount
return np.array(z)
def adam_svrg(loss_or_grads, params, learning_rate=0.001, beta1=0.9,
beta2=0.999, epsilon=1e-8):
all_grads = get_or_compute_grads(loss_or_grads, params)
t_prev = []
updates = []
updates_of = []
grads_adam = []
for m_r in range(2):
t_prev.append(theano.shared(utils.floatX(0.)))
updates.append(OrderedDict())
updates_of.append(OrderedDict())
# Using theano constant to prevent upcasting of float32
one = TT.constant(1)
t = t_prev[-1] + 1
if (m_r==0):
a_t = learning_rate*TT.sqrt(one-beta2**t)/(one-beta1**t)
else:
beta2 = 0.9
a_t = learning_rate/4*TT.sqrt(one-beta2**t)/(one-beta1**t)
i = 0
l = []
h = []
for param, g_t in zip(params, all_grads):
value = param.get_value(borrow=True)
m_prev = theano.shared(np.zeros(value.shape, dtype=value.dtype),
broadcastable=param.broadcastable)
v_prev = theano.shared(np.zeros(value.shape, dtype=value.dtype),
broadcastable=param.broadcastable)
m_t = beta1*m_prev + (one-beta1)*g_t
v_t = beta2*v_prev + (one-beta2)*g_t**2
step = a_t*m_t/(TT.sqrt(v_t) + epsilon)
# eff_step = TT.sum(TT.square(step,None))
h.append(TT.sum(TT.square(step)))
l.append(TT.sum(TT.square(m_t)))
updates[-1][m_prev] = m_t
updates[-1][v_prev] = v_t
updates_of[-1][param] = param - step
i+=1
updates[-1][t_prev[-1]] = t
grads_adam.append(TT.sqrt((h[0]+h[1]+h[2]+h[3]+h[4]+h[5]+h[6])/(l[0]+l[1]+l[2]+l[3]+l[4]+l[5]+l[6])))
return updates_of,grads_adam
load_policy=True
# normalize() makes sure that the actions for the environment lies
# within the range [-1, 1] (only works for environments with continuous actions)
env = normalize(GymEnv("InvertedPendulum-v1"))
# Initialize a neural network policy with a single hidden layer of 8 hidden units
policy = GaussianMLPPolicy(env.spec, hidden_sizes=(32,32))
snap_policy = GaussianMLPPolicy(env.spec, hidden_sizes=(32,32))
back_up_policy = GaussianMLPPolicy(env.spec, hidden_sizes=(32,32))
parallel_sampler.populate_task(env, policy)
# policy.distribution returns a distribution object under rllab.distributions. It contains many utilities for computing
# distribution-related quantities, given the computed dist_info_vars. Below we use dist.log_likelihood_sym to compute
# the symbolic log-likelihood. For this example, the corresponding distribution is an instance of the class
# rllab.distributions.DiagonalGaussian
dist = policy.distribution
snap_dist = snap_policy.distribution
# We will collect 100 trajectories per iteration
N = 100
# Each trajectory will have at most 100 time steps
T = 1000
#We will collect M secondary trajectories
M = 10
#Number of sub-iterations
#m_itr = 100
# Number of iterations
#n_itr = np.int(10000/(m_itr*M+N))
# Set the discount factor for the problem
discount = 0.995
# Learning rate for the gradient update
#learning_rate = 0.00005
learning_rate = 0.001
s_tot = 10000
observations_var = env.observation_space.new_tensor_variable(
'observations',
# It should have 1 extra dimension since we want to represent a list of observations
extra_dims=1
)
actions_var = env.action_space.new_tensor_variable(
'actions',
extra_dims=1
)
d_rewards_var = TT.vector('d_rewards')
importance_weights_var = TT.vector('importance_weight')
# policy.dist_info_sym returns a dictionary, whose values are symbolic expressions for quantities related to the
# distribution of the actions. For a Gaussian policy, it contains the mean and (log) standard deviation.
dist_info_vars = policy.dist_info_sym(observations_var)
snap_dist_info_vars = snap_policy.dist_info_sym(observations_var)
surr = TT.sum(- dist.log_likelihood_sym_1traj_GPOMDP(actions_var, dist_info_vars) * d_rewards_var)
params = policy.get_params(trainable=True)
snap_params = snap_policy.get_params(trainable=True)
importance_weights = dist.likelihood_ratio_sym_1traj_GPOMDP(actions_var,dist_info_vars,snap_dist_info_vars)
grad = theano.grad(surr, params)
eval_grad1 = TT.matrix('eval_grad0',dtype=grad[0].dtype)
eval_grad2 = TT.vector('eval_grad1',dtype=grad[1].dtype)
eval_grad3 = TT.matrix('eval_grad3',dtype=grad[2].dtype)
eval_grad4 = TT.vector('eval_grad4',dtype=grad[3].dtype)
eval_grad5 = TT.matrix('eval_grad5',dtype=grad[3].dtype)
eval_grad6 = TT.vector('eval_grad5',dtype=grad[3].dtype)
eval_grad7 = TT.vector('eval_grad5',dtype=grad[3].dtype)
surr_on1 = TT.sum(dist.log_likelihood_sym_1traj_GPOMDP(actions_var,snap_dist_info_vars)*d_rewards_var*importance_weights_var)
surr_on2 = TT.sum(-snap_dist.log_likelihood_sym_1traj_GPOMDP(actions_var,dist_info_vars)*d_rewards_var)
grad_imp = theano.grad(surr_on1,snap_params)
update,step =adam_svrg([eval_grad1, eval_grad2, eval_grad3, eval_grad4, eval_grad5,eval_grad6,eval_grad7], params, learning_rate=learning_rate)
f_train = theano.function(
inputs = [observations_var, actions_var, d_rewards_var],
outputs = grad
)
f_update = [theano.function(
inputs = [eval_grad1, eval_grad2, eval_grad3, eval_grad4, eval_grad5,eval_grad6,eval_grad7],
outputs = step[n_sub_iter],
updates = update[n_sub_iter]
) for n_sub_iter in range(2)]
f_importance_weights = theano.function(
inputs = [observations_var, actions_var],
outputs = importance_weights
)
f_update_SVRG = [theano.function(
inputs = [eval_grad1, eval_grad2, eval_grad3, eval_grad4, eval_grad5,eval_grad6,eval_grad7],
outputs = step[n_sub_iter],
updates = update[n_sub_iter]
) for n_sub_iter in range(2)]
f_imp_SVRG = theano.function(
inputs=[observations_var, actions_var, d_rewards_var, importance_weights_var],
outputs=grad_imp,
)
alla = {}
variance_svrg_data={}
variance_sgd_data={}
importance_weights_data={}
rewards_snapshot_data={}
rewards_subiter_data={}
n_sub_iter_data={}
diff_lr_data = {}
alfa_t_data = {}
parallel_sampler.initialize(3)
for k in range(10):
if (load_policy):
snap_policy.set_param_values(np.loadtxt('policy_ip.txt'), trainable=True)
policy.set_param_values(np.loadtxt('policy_ip.txt'), trainable=True)
else:
policy.set_param_values(snap_policy.get_param_values(trainable=True), trainable=True)
avg_return = []
#np.savetxt("policy_novar.txt",snap_policy.get_param_values(trainable=True))
n_sub_iter=[]
rewards_sub_iter=[]
rewards_snapshot=[]
importance_weights=[]
variance_svrg = []
variance_sgd = []
diff_lr = []
alfa_t = []
j=0
while j<s_tot-N:
paths = parallel_sampler.sample_paths_on_trajectories(policy.get_param_values(),N,T,show_bar=False)
paths = paths[:N]
#baseline.fit(paths)
j+=N
observations = [p["observations"] for p in paths]
actions = [p["actions"] for p in paths]
d_rewards = [p["rewards"] for p in paths]
temp = list()
for x in d_rewards:
z=list()
t=1
for y in x:
z.append(y*t)
t*=discount
temp.append(np.array(z))
d_rewards=temp
s_g = f_train(observations[0], actions[0], d_rewards[0])
s_g_fv = [unpack(s_g)]
for ob,ac,rw in zip(observations[1:],actions[1:],d_rewards[1:]):
i_g = f_train(ob, ac, rw)
s_g_fv.append(unpack(i_g))
s_g = [sum(x) for x in zip(s_g,i_g)]
s_g = [x/len(paths) for x in s_g]
stp_snp = np.sum(f_update[0](s_g[0],s_g[1],s_g[2],s_g[3],s_g[4],s_g[5],s_g[6]))
rewards_snapshot.append(np.array([sum(p["rewards"]) for p in paths]))
avg_return.append(np.mean([sum(p["rewards"]) for p in paths]))
var_4_fg = np.cov(s_g_fv,rowvar=False)
var_fg = var_4_fg/(N)
print(str(j-1)+' Snapshot! Average Return:', avg_return[-1])
print("step snapshot:", stp_snp)
back_up_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
n_sub = 0
while j<s_tot-M:
j += M
sub_paths = parallel_sampler.sample_paths_on_trajectories(policy.get_param_values(),M,T,show_bar=False)
sub_paths[:M] = sub_paths[:M]
#baseline.fit(paths)
sub_observations=[p["observations"] for p in sub_paths]
sub_actions = [p["actions"] for p in sub_paths]
sub_d_rewards = [p["rewards"] for p in sub_paths]
temp = list()
for x in sub_d_rewards:
z=list()
t=1
for y in x:
z.append(y*t)
t*=discount
temp.append(np.array(z))
sub_d_rewards=temp
n_sub+=1
s_g_sgd = f_train(sub_observations[0], sub_actions[0], sub_d_rewards[0])
s_g_fv_sgd = [unpack(s_g_sgd)]
iw_var = f_importance_weights(sub_observations[0], sub_actions[0])
s_g_is = f_imp_SVRG(sub_observations[0], sub_actions[0], sub_d_rewards[0],iw_var)
s_g_fv_is = [unpack(s_g_is)]
w_cum=np.sum(dis_iw(iw_var))
for ob,ac,rw in zip(sub_observations[1:],sub_actions[1:],sub_d_rewards[1:]):
i_g_sgd = f_train(ob, ac, rw)
s_g_fv_sgd.append(unpack(i_g_sgd))
s_g_sgd = [sum(x) for x in zip(s_g_sgd,i_g_sgd)]
iw_var = f_importance_weights(ob, ac)
s_g_is_sgd = f_imp_SVRG(ob, ac, rw,iw_var)
s_g_fv_is.append(unpack(s_g_is_sgd))
s_g_is = [sum(x) for x in zip(s_g_is,s_g_is_sgd)]
w_cum+=np.sum(dis_iw(iw_var))
# print("w cum: " + str(w_cum))
s_g_is = [x/w_cum for x in s_g_is]
s_g_sgd = [x/len(sub_paths) for x in s_g_sgd]
var_sgd = np.cov(s_g_fv_sgd,rowvar=False)
var_batch = var_sgd/(M)
var_is_sgd = np.cov(s_g_fv_is,rowvar=False)
var_is = var_is_sgd/(w_cum)
m_is = np.mean(s_g_fv_is,axis=0)
m_sgd = np.mean(s_g_fv_sgd,axis=0)
cov= np.outer(s_g_fv_is[0]-m_is,s_g_fv_sgd[0]-m_sgd)
for i in range(M-1):
cov += np.outer(s_g_fv_is[i+1]-m_is,s_g_fv_sgd[i+1]-m_sgd)
for i in range(M):
cov += np.outer(s_g_fv_sgd[i]-m_sgd,s_g_fv_is[i]-m_is)
cov = cov/(M*np.sqrt(M*w_cum))
var_svrg = var_is + var_batch + cov
var_dif = var_svrg-var_batch
print("var is: " + str(np.trace(var_is)))
print("cov: " + str(np.trace(cov)))
iw = f_importance_weights(sub_observations[0],sub_actions[0])
importance_weights.append(np.mean(iw))
variance_svrg.append((np.diag(var_svrg).sum()))
variance_sgd.append((np.diag(var_batch).sum()))
rewards_sub_iter.append(np.array([sum(p["rewards"]) for p in sub_paths]))
avg_return.append(np.mean([sum(p["rewards"]) for p in sub_paths]))
back_up_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
g = [sum(x) for x in zip(s_g_is,s_g_sgd,s_g)]
print(str(j-1)+' Average Return:', avg_return[-1])
stp = np.sum(f_update[1](g[0],g[1],g[2],g[3],g[4],g[5],g[6]))
print("step:",stp)
diff_lr.append(stp/M-stp_snp/N)
alfa_t.append(stp)
if (stp/M<stp_snp/N or n_sub+1>= max_sub_iter):
break
n_sub_iter.append(n_sub)
snap_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
# plt.plot(avg_return)
# plt.show()
rewards_subiter_data["rewardsSubIter"+str(k)]=rewards_sub_iter
rewards_snapshot_data["rewardsSnapshot"+str(k)]= rewards_snapshot
n_sub_iter_data["nSubIter"+str(k)]= n_sub_iter
variance_sgd_data["variancceSgd"+str(k)] = variance_sgd
variance_svrg_data["varianceSvrg"+str(k)]=variance_svrg
importance_weights_data["importanceWeights"+str(k)] = importance_weights
diff_lr_data["diffLr"+str(k)] = diff_lr
alfa_t_data["alfaT"+str(k)] = alfa_t
avg_return=np.array(avg_return)
#plt.plot(avg_return)
#plt.show()
print('Fine sessione: ', str(k))
alla["avgReturn"+str(k)]=avg_return
alla = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in alla.items() ]))
rewards_subiter_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in rewards_subiter_data.items() ]))
rewards_snapshot_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in rewards_snapshot_data.items() ]))
n_sub_iter_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in n_sub_iter_data.items() ]))
variance_sgd_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in variance_sgd_data.items() ]))
variance_svrg_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in variance_svrg_data.items() ]))
importance_weights_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in importance_weights_data.items() ]))
diff_lr_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in diff_lr_data.items() ]))
alfa_t_data = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in alfa_t_data.items() ]))
rewards_subiter_data.to_csv("rewards_subiter_swimmer_vA_sn2.csv",index=False)
rewards_snapshot_data.to_csv("rewards_snapshot_swimmer_vA_sn2.csv",index=False)
n_sub_iter_data.to_csv("n_sub_iter_vA_swimmer_sn2.csv",index=False)
variance_sgd_data.to_csv("variance_sgd_vA_swimmer_sn2.csv",index=False)
variance_svrg_data.to_csv("variance_svrg_vA_swimmer_sn2.csv",index=False)
importance_weights_data.to_csv("importance_weights_vA_swimmer_sn2.csv",index=False)
diff_lr_data.to_csv("diff_lr_sn2.csv")
alfa_t_data.to_csv("alfa_t_sn2.csv")
alla.to_csv("GPOMDP_SVRG_adaptive_m06_verA_swimmer_sn2.csv",index=False)