Q_next = Q_target.evaluate(sess, stag_batch)
Q_next_max = np.amax(Q_next, 1)
a_batch = a_batch.astype(int)
for i in range(MINI_BATCH):
Y[i, a_batch[i, 0]] = r_batch[
i, 0] + GAMMA * Q_next_max[i] * (1 - terminal_batch[i])
# train on estimated Q next and rewards
error = Q.train(sess, s_batch, Y)
if Done is True:
break
# run validation simulations
L.AddRecord('network_left',
simulator.SimulateNeuralEpisode(Q, sess, env_left, False))
L.AddRecord('network_middle',
simulator.SimulateNeuralEpisode(Q, sess, env_middle, False))
L.AddRecord('network_right',
simulator.SimulateNeuralEpisode(Q, sess, env_right, False))
L.AddRecord(
'policy_left',
simulator.SimulatePolicyEpisode(policy, discretizer, env_left, False))
L.AddRecord(
'policy_middle',
simulator.SimulatePolicyEpisode(policy, discretizer, env_middle,
False))
L.AddRecord(
'policy_right',
simulator.SimulatePolicyEpisode(policy, discretizer, env_right, False))
error = Q.train_output(sess, s_batch, Y)
else:
error = Q.train(sess, s_batch, Y)
E_local.append(error)
E_local = sum(E_local)/len(E_local)
totalE += E_local
if Done is True:
break
totalE = totalE/t
# run validation simulations
a = simulator.SimulateNeuralEpisode(Q, sess, env_left, False)
L.AddRecord('network_left', a)
L.AddRecord('network_middle',simulator.SimulateNeuralEpisode(Q, sess, env_middle, False))
L.AddRecord('network_right',simulator.SimulateNeuralEpisode(Q, sess, env_right, False))
L.AddRecord('policy_left',simulator.SimulatePolicyEpisode(policy,discretizer, env_left, False))
L.AddRecord('policy_middle',simulator.SimulatePolicyEpisode(policy,discretizer, env_middle, False))
L.AddRecord('policy_right',simulator.SimulatePolicyEpisode(policy,discretizer, env_right, False))
L.AddRecord('total_reward', totalR)
L.AddRecord('error', totalE)
s_est, _, _, _, _, num = R.SampleMiniBatch(V_EST)
Q_est_arg = Q.evaluate(sess, s_est)
Q_est_argmax = np.argmax(Q_est_arg,1)*1.0
V_est = Q_est_argmax.sum()/num*1.0
L.AddRecord('estimated_value', V_est)
# update target network
if steps >= C_STEPS:
temp = [] for l in xrange(numlogs): temp.append(all_logs[l][i]) avg.append(sum(temp)/len(temp)*1.0) avgep = [] var = 0 avgep_up = [] avgep_down = [] for i in xrange(length-LEN): a = sum(avg[i:i+LEN])/LEN*1.0 var = np.sqrt(np.var(avg[i:i+LEN])) avgep.append(a) avgep_up.append(avgep[i] + var) avgep_down.append(avgep[i] - var) L.AddRecord(logname,avgep[i]) L.AddRecord(logname+'_up',avgep_up[i]) L.AddRecord(logname+'_down',avgep_down[i]) L.Save(OUTLOG) t = np.arange(1,len(avgep)+1) var_up = np.array(avgep_up) var_down = np.array(avgep_down) plt.figure(1) plt.fill_between(t,var_down, var_up, facecolor='blue', linewidth=0.0,alpha=0.5) plt.plot(t,avgep,'b') plt.show()
def train(sess, env, actor, critic):
env_left = gym.make(ENV_LEFT)
env_middle = gym.make(ENV_MIDDLE)
env_right = gym.make(ENV_RIGHT)
L = Logger()
log_not_empty = L.Load(LOG_FILE)
if log_not_empty:
print("Log file loaded")
else:
("Creating new log file")
L.AddNewLog('network_left')
L.AddNewLog('network_middle')
L.AddNewLog('network_right')
L.AddNewLog('total_reward')
L.AddNewLog('estimated_value')
L.AddNewLog('network_random')
simulator = Simulator(MAX_EP_STEPS, STATE, 1, -0.5, None)
# Set up summary Ops
summary_ops, summary_vars = build_summaries()
sess.run(tf.initialize_all_variables())
writer = tf.train.SummaryWriter(SUMMARY_DIR, sess.graph)
# Initialize target network weights
actor.update_target_network()
critic.update_target_network()
# Initialize replay memory
replay_buffer = ReplayBuffer(BUFFER_SIZE, RANDOM_SEED)
n = OUnoise(INPUT)
for i in xrange(MAX_EPISODES):
s = env.reset()
ep_reward = 0
ep_ave_max_q = 0
n.Reset()
for j in xrange(MAX_EP_STEPS):
if RENDER_ENV:
env.render()
# Added exploration noise
#a = actor.predict(np.reshape(s, (1, 8))) + (1. / (1. + i + j))
a = actor.predict(np.reshape(s, (1, STATE))) + n.Sample()
s2, r, terminal, info = env.step(a[0])
r += -0.5
replay_buffer.add(np.reshape(s, (actor.s_dim,)), np.reshape(a, (actor.a_dim,)), r, \
terminal, np.reshape(s2, (actor.s_dim,)))
# Keep adding experience to the memory until
# there are at least minibatch size samples
if replay_buffer.size() > MINIBATCH_SIZE:
s_batch, a_batch, r_batch, t_batch, s2_batch = \
replay_buffer.sample_batch(MINIBATCH_SIZE)
# Calculate targets
target_q = critic.predict_target(
s2_batch, actor.predict_target(s2_batch))
y_i = []
for k in xrange(MINIBATCH_SIZE):
if t_batch[k]:
y_i.append(r_batch[k])
else:
y_i.append(r_batch[k] + GAMMA * target_q[k])
# Update the critic given the targets
predicted_q_value, _ = critic.train(
s_batch, a_batch, np.reshape(y_i, (MINIBATCH_SIZE, 1)))
ep_ave_max_q += np.amax(predicted_q_value)
# Update the actor policy using the sampled gradient
a_outs = actor.predict(s_batch)
grads = critic.action_gradients(s_batch, a_outs)
actor.train(s_batch, grads[0])
# Update target networks
actor.update_target_network()
critic.update_target_network()
s = s2
ep_reward += r
if terminal:
break
summary_str = sess.run(summary_ops,
feed_dict={
summary_vars[0]: ep_reward,
summary_vars[1]: ep_ave_max_q / float(j)
})
writer.add_summary(summary_str, i)
writer.flush()
print 'episode ', i, ' | Reward: %.2i' % int(ep_reward), " | Episode", i, \
'| Qmax: %.4f' % (ep_ave_max_q / float(j))
# log statistics
L.AddRecord(
'network_left',
simulator.SimulateContNeuralEpisode(actor, sess, env_left, False))
L.AddRecord(
'network_middle',
simulator.SimulateContNeuralEpisode(actor, sess, env_middle,
False))
L.AddRecord(
'network_right',
simulator.SimulateContNeuralEpisode(actor, sess, env_right, False))
temp_r = 0
for rand_i in xrange(10):
temp_r = temp_r + simulator.SimulateContNeuralEpisode(
actor, sess, env, False) * 0.1
L.AddRecord('network_random', temp_r)
L.AddRecord('total_reward', ep_reward)
if replay_buffer.size() > V_EST:
num = V_EST
else:
num = replay_buffer.size()
s_batch, a_batch, r_batch, t_batch, s2_batch = replay_buffer.sample_batch(
num)
Q = critic.predict(s_batch, actor.predict(s_batch))
V_est = Q.sum() / num * 1.0
L.AddRecord('estimated_value', V_est)
if i % SAVE_RATE == 0:
L.Save(LOG_FILE)
i, 0] + GAMMA * Q_next_max[i] * (1 - terminal_batch[i])
# train on estimated Q next and rewards
error = Q.train(sess, s_batch, Y)
E_local.append(error)
E_local = sum(E_local) / len(E_local)
totalE += E_local
if Done is True:
break
totalE = totalE / t
# run validation simulations
L.AddRecord('network_left',
simulator.SimulateNeuralEpisode(Q, sess, env_left, False))
L.AddRecord('network_middle',
simulator.SimulateNeuralEpisode(Q, sess, env_middle, False))
L.AddRecord('network_right',
simulator.SimulateNeuralEpisode(Q, sess, env_right, False))
temp_r = 0
for rand_i in xrange(10):
temp_r = temp_r + simulator.SimulateNeuralEpisode(Q, sess, env,
False) * 0.1
L.AddRecord('network_random', temp_r)
L.AddRecord('total_reward', totalR)
L.AddRecord('error', totalE)
s_est, _, _, _, _, num = R.SampleMiniBatch(V_EST)
Q_est_arg = Q.evaluate(sess, s_est)
Q_est_argmax = np.argmax(Q_est_arg, 1) * 1.0
V_est = Q_est_argmax.sum() / num * 1.0