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GPOMDP_SVRG_B_V.py
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GPOMDP_SVRG_B_V.py
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from rllab.envs.box2d.cartpole_env import CartpoleEnv
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
import matplotlib.pyplot as plt
from rllab.envs.gym_env import GymEnv
import pandas as pd
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 = np.concatenate((res,i_g_arr[2][0]))
res = np.concatenate((res,i_g_arr[3]))
return res
def compute_snap_batch(observations,actions,d_rewards,n_traj,n_part):
n=n_traj
i=0
svrg_snap=list()
while(n-np.int(n_traj/n_part)>=0):
n=n-np.int(n_traj/n_part)
s_g = f_train(observations[i], actions[i], d_rewards[i])
for s in range(i+1,i+np.int(n_traj/n_part)):
s_g = [sum(x) for x in zip(s_g,f_train(observations[s], actions[s], d_rewards[s]))]
s_g = [x/np.int(n_traj/n_part) for x in s_g]
i += np.int(n_traj/n_part)
svrg_snap.append(unpack(s_g))
return svrg_snap
def estimate_variance(observations,actions,d_rewards,snap_grads,n_traj,n_traj_s,n_part,M,N):
n=n_traj
i=0
svrg=list()
j=0
while(n-np.int(n_traj/n_part)>=0):
n=n-np.int(n_traj/n_part)
iw = f_importance_weights(observations[i],actions[i])
x = unpack(f_train_SVRG_4v(observations[i],actions[i],d_rewards[i],iw))*np.sqrt(np.int(n_traj/n_part)/M)
g = snap_grads[j]*np.sqrt(np.int(n_traj_s/n_part)/N)+x
for s in range(i+1,i+np.int(n_traj/n_part)):
iw = f_importance_weights(observations[s],actions[s])
g_prov=unpack(f_train_SVRG_4v(observations[s],actions[s],d_rewards[s],iw))*np.sqrt(np.int(n_traj/n_part)/M)
g+=snap_grads[j]*np.sqrt(np.int((n_traj_s)/n_part)/N) + g_prov
g=g/n_traj*n_part
i+=np.int(n_traj/n_part)
j+=1
svrg.append(g)
return (np.diag(np.cov(np.matrix(svrg),rowvar=False)).sum())
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(CartpoleEnv())
#env = GymEnv("InvertedPendulum-v1")
# Initialize a neural network policy with a single hidden layer of 8 hidden units
policy = GaussianMLPPolicy(env.spec, hidden_sizes=(8,),learn_std=True)
snap_policy = GaussianMLPPolicy(env.spec, hidden_sizes=(8,),learn_std=True)
back_up_policy = GaussianMLPPolicy(env.spec, hidden_sizes=(8,),learn_std=True)
parallel_sampler.populate_task(env, snap_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 = 100
#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 = 0.99
# Learning rate for the gradient update
learning_rate = 0.00005
#perc estimate
perc_est = 0.6
#tot trajectories
s_tot = 10000
partition = 3
porz = np.int(perc_est*N)
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,snap_dist_info_vars,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.col('eval_grad3',dtype=grad[2].dtype)
eval_grad4 = TT.vector('eval_grad4',dtype=grad[3].dtype)
eval_grad5 = TT.vector('eval_grad4',dtype=grad[4].dtype)
surr_on1 = TT.sum(- dist.log_likelihood_sym_1traj_GPOMDP(actions_var,dist_info_vars)*d_rewards_var*importance_weights_var)
surr_on2 = TT.sum(snap_dist.log_likelihood_sym_1traj_GPOMDP(actions_var,snap_dist_info_vars)*d_rewards_var)
grad_SVRG =[sum(x) for x in zip([eval_grad1, eval_grad2, eval_grad3, eval_grad4,eval_grad5], theano.grad(surr_on1,params),theano.grad(surr_on2,snap_params))]
grad_SVRG_4v = [sum(x) for x in zip(theano.grad(surr_on1,params),theano.grad(surr_on2,snap_params))]
grad_var = theano.grad(surr_on1,params)
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],
outputs = None,
updates = sgd([eval_grad1, eval_grad2, eval_grad3, eval_grad4,eval_grad5], params, learning_rate=learning_rate)
)
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],
outputs = None,
updates = sgd([eval_grad1, eval_grad2, eval_grad3, eval_grad4,eval_grad5], params, learning_rate=learning_rate)
)
f_train_SVRG = theano.function(
inputs=[observations_var, actions_var, d_rewards_var, eval_grad1, eval_grad2, eval_grad3, eval_grad4,eval_grad5,importance_weights_var],
outputs=grad_SVRG,
)
f_train_SVRG_4v = theano.function(
inputs=[observations_var, actions_var, d_rewards_var,importance_weights_var],
outputs=grad_SVRG_4v,
)
var_SVRG = theano.function(
inputs=[observations_var, actions_var, d_rewards_var, importance_weights_var],
outputs=grad_var,
)
alla = {}
variance_svrg_data={}
variance_sgd_data={}
importance_weights_data={}
rewards_snapshot_data={}
rewards_subiter_data={}
n_sub_iter_data={}
for k in range(10):
if (load_policy):
snap_policy.set_param_values(np.loadtxt('policy.txt'), trainable=True)
policy.set_param_values(np.loadtxt('policy.txt'), trainable=True)
avg_return = list()
n_sub_iter=[]
rewards_sub_iter=[]
rewards_snapshot=[]
importance_weights=[]
variance_svrg = []
variance_sgd = []
#np.savetxt("policy_novar.txt",snap_policy.get_param_values(trainable=True))
j=0
while j<s_tot-N:
paths = parallel_sampler.sample_paths_on_trajectories(snap_policy.get_param_values(),N,T,show_bar=False)
#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]
b=compute_snap_batch(observations[0:porz],actions[0:porz],d_rewards[0:porz],porz,partition)
f_update(s_g[0],s_g[1],s_g[2],s_g[3],s_g[4])
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_sgd = np.cov(np.matrix(b),rowvar=False)
var_batch = (var_sgd)*(porz/partition)/M
print(str(j-1)+' Average Return:', avg_return[-1])
back_up_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
n_sub = 0
while j<s_tot-M:
iw_var = f_importance_weights(observations[0],actions[0])
s_g_is = var_SVRG(observations[0], actions[0], d_rewards[0],iw_var)
s_g_fv_is = [unpack(s_g_is)]
for ob,ac,rw in zip(observations[1:],actions[1:],d_rewards[1:]):
iw_var = f_importance_weights(ob, ac)
s_g_is = var_SVRG(ob, ac, rw,iw_var)
s_g_fv_is.append(unpack(s_g_is))
var_svrg = (estimate_variance(observations[porz:],actions[porz:],d_rewards[porz:],b,N-porz,porz,partition,M,N))
var_dif = var_svrg-(np.diag(var_batch).sum())
#eigval = np.real(np.linalg.eig(var_dif)[0])
if (var_dif>0 or np.mean(iw_var)<0.6):
policy.set_param_values(back_up_policy.get_param_values(trainable=True), trainable=True)
break
variance_svrg.append(var_svrg)
variance_sgd.append((np.diag(var_batch).sum()))
#print(np.sum(eigval))
j += M
n_sub+=1
sub_paths = parallel_sampler.sample_paths_on_trajectories(snap_policy.get_param_values(),M,T,show_bar=False)
#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
iw = f_importance_weights(sub_observations[0],sub_actions[0])
importance_weights.append(np.mean(iw))
back_up_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
g = f_train_SVRG(sub_observations[0],sub_actions[0],sub_d_rewards[0],s_g[0],s_g[1],s_g[2],s_g[3],s_g[4],iw)
for ob,ac,rw in zip(sub_observations[1:],sub_actions[1:],sub_d_rewards[1:]):
iw = f_importance_weights(ob,ac)
importance_weights.append(np.mean(iw))
g = [sum(x) for x in zip(g,f_train_SVRG(ob,ac,rw,s_g[0],s_g[1],s_g[2],s_g[3],s_g[4],iw))]
g = [x/len(sub_paths) for x in g]
f_update(g[0],g[1],g[2],g[3],g[4])
p=snap_policy.get_param_values(trainable=True)
s_p = parallel_sampler.sample_paths_on_trajectories(policy.get_param_values(),10,T,show_bar=False)
snap_policy.set_param_values(p,trainable=True)
rewards_sub_iter.append(np.array([sum(p["rewards"]) for p in s_p]))
avg_return.append(np.mean([sum(p["rewards"]) for p in s_p]))
#print(str(j)+' Average Return:', avg_return[j])
n_sub_iter.append(n_sub)
snap_policy.set_param_values(policy.get_param_values(trainable=True), trainable=True)
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
avg_return=np.array(avg_return)
#plt.plot(avg_return)
#plt.show()
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() ]))
rewards_subiter_data.to_csv("rewards_subiter_avar.csv",index=False)
rewards_snapshot_data.to_csv("rewards_snapshot_avar.csv",index=False)
n_sub_iter_data.to_csv("n_sub_iter_avar.csv",index=False)
variance_sgd_data.to_csv("variance_sgd_avar.csv",index=False)
variance_svrg_data.to_csv("variance_svrg_avar.csv",index=False)
importance_weights_data.to_csv("importance_weights_avar.csv",index=False)
alla.to_csv("GPOMDP_ADAPTIVE_SVRG_avar.csv",index=False)
#gpomdp = np.loadtxt("GPOMDP_l5e-05")
#gpomdp_svrg=np.loadtxt("GPOMDP_SVRG_5e-5")
#gpomdp_svrg_ada_wb = np.loadtxt("GPOMDP_SVRG_5e-5_ada_wb")
#gpomdp_svrg_ada_wb_bv = alla_mean
#
#plt.plot(gpomdp)
#plt.plot(gpomdp_svrg)
#plt.plot(gpomdp_svrg_ada_wb[::10])
#plt.plot(gpomdp_svrg_ada_wb_bv[::10])
#plt.legend(['gpomdp','gpomdp_svrg','gpomdp_svrg_ada_wb','gpomdp_svrg_wb_bv'], loc='lower right')
#plt.savefig("adapt.jpg", figsize=(32, 24), dpi=160)
##plt.show()
#uni = np.ones(640,dtype=np.int)
#for i in range(40):
# uni[i*16]=10
#scal_svrg = np.repeat(gpondp_svrg,uni)
#plt.plot(gpondp)
#plt.plot(scal_svrg )
#plt.legend(['gpondp','gpondp_svrg'], loc='lower right')
#plt.savefig("gpondp_5e-6.jpg", figsize=(32, 24), dpi=160)