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NAF_mc.py
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NAF_mc.py
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import tensorflow as tf
from matplotlib import pyplot as plt
import numpy as np
from Network import Network
from Noise import OUNoise
from ReplayBuffer import ReplayBuffer
from collections import defaultdict
class NAF:
def __init__(self, sess, env, learning_rate, tau, gamma,
buffer_size, random_seed, summary_dir, sigma_P_dep, det, qnaf, scope,
hn=0, ac=True, sep_V=True,):
self.env = env
self.s_dim = self.env.observation_space.shape[0]
self.a_dim = self.env.action_space.shape[0]
self.gamma = gamma
self.tau = tau
self.random_seed = random_seed
self.sess = sess
self.summary_dir = summary_dir
self.det = det
trainables_pre_naf = len(tf.trainable_variables())
self.model = Network(sess, self.s_dim, self.a_dim, learning_rate,
trainables_pre_naf, scope + 'model', sigma_P_dep, det, hn)
trainables_model = len(tf.trainable_variables())
self.target_model = Network(sess, self.s_dim, self.a_dim, learning_rate,
trainables_model, scope + 'tmodel', sigma_P_dep, det, hn)
self.target_model.make_soft_update_from(self.model, self.tau)
trainables_target = len(tf.trainable_variables())
if sep_V:
trainables_pre_naf = len(tf.trainable_variables())
self.critic = Network(sess, self.s_dim, self.a_dim, learning_rate,
trainables_pre_naf, scope + 'cmodel', False, True, 0)
self.init = tf.global_variables_initializer()
self.sess.run(self.init)
self.buffer = ReplayBuffer(buffer_size, random_seed)
self.saver = tf.train.Saver()
#create lists to contain total rewards and steps per episode
self.rewards = []
self.episodes_xs = []
self.episodes_us = []
self.episodes_rs = []
self.episodes_Ps = []
self.episodes_Vs = []
self.episodes_Qs = []
self.episodes_V_s = []
self.episodes_ss = []
self.env.reset()
self.qNAF = qnaf
self.dep = sigma_P_dep
self.reinforce = False
self.ac = ac
self.calc_c = qnaf and self.dep and (not self.det) and (hn == 0)
self.separate_V = sep_V
self.r_xs = []
self.r_us = []
self.r_rs = []
self.episodes_ls = []
print("end init")
def update_target_network(self):
self.sess.run(self.update_target_network_params)
def run_n_episodes(self, num_episodes, max_ep_length,
minibatch_size, explore=True, num_updates = 5, summary_checkpoint=1, eta=0.01, num_updates_ac=1, T=1): #num_updates from article
for i in range(num_episodes):
noise = OUNoise(self.a_dim, sigma=3, theta=0.2)
x = self.env.reset()
x = x.reshape(1, -1)
u = np.zeros(self.s_dim)
t = False
episodes_reward = 0
#for REINFORCE
self.r_rs.append([])
self.r_xs.append([])
self.r_us.append([])
self.episodes_ls.append(0)
while True:
self.episodes_ls[-1] = self.episodes_ls[-1] + 1
if self.det:
u, V = self.sess.run((self.model.mu_det, self.model.V),
feed_dict={self.model.inputs_x: x})
self.episodes_Vs.append(V)
else:
u, P, sigma, V = self.sess.run((self.model.mu_norm, self.model.P, self.model.sigma, self.model.V),
feed_dict={self.model.inputs_x: x})
self.episodes_Ps.append(P)
self.episodes_ss.append(sigma)
self.episodes_Vs.append(V)
if self.separate_V:
self.episodes_V_s.append(self.critic.predict_V_sep(x))
if explore:
u += noise.noise()
u = np.clip(u, -1.0, 1.0)
u = u.reshape(1, -1)
x1, r, t, info = self.env.step(u.reshape(-1))
self.r_xs[-1].append(x)
self.r_us[-1].append(u)
self.r_rs[-1].append(r)
episodes_reward += r
self.buffer.add(x.reshape(1, -1), u, r, t, x1.reshape(1, -1))
self.episodes_xs.append(x)
self.episodes_us.append(u)
self.episodes_rs.append(r)
#Actor-Critic
x = x1.reshape(1, -1)
if self.qNAF:
for k in range(num_updates):
x_batch, u_batch, r_batch, t_batch, x1_batch = \
self.buffer.sample_batch(minibatch_size)
x_batch, u_batch, r_batch, t_batch, x1_batch = \
x_batch.reshape(-1, self.s_dim), u_batch.reshape(-1, self.a_dim), r_batch.reshape(-1, 1),\
t_batch.reshape(-1), x1_batch.reshape(-1, self.s_dim)
if self.qNAF:
y_batch = self.gamma * self.target_model.predict_V(x1_batch) + r_batch
self.model.update_Q(x_batch, u_batch, y_batch)
self.target_model.soft_update_from(self.model)
if t:
break
if self.ac:
r_xs_l = np.array(self.r_xs[-1]).reshape(-1, self.s_dim)
r_rs_l = np.array(self.r_rs[-1]).reshape(-1, 1)
for idx in range(2, len(r_rs_l) + 1):
r_rs_l[-idx] += self.gamma * r_rs_l[-idx + 1]
self.r_rs[-1] = r_rs_l
r_rs_ = np.array(self.r_rs).reshape(-1, 1)
r_xs_ = np.array(self.r_xs).reshape(-1, self.s_dim)
r_us_ = np.array(self.r_us).reshape(-1, self.a_dim)
for _ in range(num_updates_ac):
#update V every episode
if self.separate_V:
self.critic.update_V_sep(r_xs_l, r_rs_l)
if i % T == 0:
#Q_target = r_rs_[:-1] + self.gamma * self.critic.predict_V_sep(r_xs_[1:])
#Q_target = np.vstack((Q_target, (r_rs_[-1])))
deltas = r_rs_
if self.separate_V:
deltas = deltas - self.critic.predict_V_sep(r_xs_)
else:
deltas = deltas - self.target_model.predict_V(r_xs_)
'''
loss = self.sess.run((self.model.loss_spg),
feed_dict={self.model.inputs_x: r_xs_,
self.model.inputs_u: r_us_,
self.model.inputs_Q: deltas})
print('loss before update', loss)
'''
self.model.update_mu(r_xs_, r_us_, deltas)
self.target_model.soft_update_from(self.model)
'''
loss = self.sess.run((self.model.loss_spg),
feed_dict={self.model.inputs_x: r_xs_,
self.model.inputs_u: r_us_,
self.model.inputs_Q: deltas})
print('loss after update', loss)
'''
#self.target_model.soft_update_from(self.model)
self.r_rs = []
self.r_xs = []
self.r_us = []
if summary_checkpoint > 0 and i % summary_checkpoint == 0:
print ('| Reward: %.2i' % int(episodes_reward), " | Episode", i)
self.plot_rewards(self.summary_dir)
self.rewards.append(episodes_reward)
def plot_rewards(self, summary_dir):
rewards = np.array(self.rewards).reshape(-1)
np.save(summary_dir + '/rewards', rewards)
np.save(summary_dir + '/episodes_xs', np.array(self.episodes_xs))
np.save(summary_dir + '/episodes_us', np.array(self.episodes_us))
np.save(summary_dir + '/episodes_rs', np.array(self.episodes_rs))
np.save(summary_dir + '/episodes_Ps', np.array(self.episodes_Ps))
np.save(summary_dir + '/episodes_Vs', np.array(self.episodes_Vs))
np.save(summary_dir + '/episodes_Qs', np.array(self.episodes_Qs))
np.save(summary_dir + '/episodes_V_s', np.array(self.episodes_V_s))
np.save(summary_dir + '/episodes_ss', np.array(self.episodes_ss))
np.save(summary_dir + '/episodes_ls', np.array(self.episodes_ls))
#plt.plot(np.arange(len(rewards)), rewards)
#plt.show()