def train(self):
for t in range(self.num_iterations):
actions, returns, losses = self.sample_model_reward_return(t)
self.sess.run(self.train_op, feed_dict={
self.action_placeholder : actions,
self.advantage_placeholder : returns-losses})
#avg_acc = np.mean(returns)
avg_acc = (np.mean(returns)*data_std + data_mean) / 100000.
#calculate number of used models:
used = 0
for key in self._used_dict.keys():
used += self._used_dict[key]
#used = np.sum(self._used_dict)
self._num_used_models.append(used)
self.log_acc.append(avg_acc)
#sigma_reward = np.sqrt(np.var(returns) / len(total_rewards))
msg = "Average accuracy within a batch: {:04.2f}".format(avg_acc*100)
self.logger.info(msg)
#print (actions)
self.logger.info("- Training done.")
#export_plot(self.log_acc, "Batch_Accuracy", 'NAS-DNN', "./batch_accuracy.png", self._num_used_models, "Sampled Model")
export_plot(self.log_acc, "Score", 'NAS-DNN', "./batch_accuracy.png")
export_plot(self._num_used_models, "Models Sampled", 'NAS-DNN', "./used_models.png")
print 'log_acc'; print self.log_acc
print '_num_used_models'; print self._num_used_models
def train(self):
"""
Performs training
You do not have to change or use anything here, but take a look
to see how all the code you've written fits together!
"""
last_eval = 0
last_record = 0
scores_eval = []
self.init_averages()
scores_eval = [] # list of scores computed at iteration time
for t in range(self.config.num_batches):
# collect a minibatch of samples
paths, total_rewards = self.sample_path(self.env)
scores_eval = scores_eval + total_rewards
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
# compute Q-val estimates (discounted future returns) for each time
# step
returns = self.get_returns(paths)
advantages = self.calculate_advantage(returns, observations)
# run training operations
if self.config.use_baseline:
self.update_baseline(returns, observations)
self.sess.run(self.train_op, feed_dict={
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.advantage_placeholder: advantages})
# tf stuff
if (t % self.config.summary_freq == 0):
self.update_averages(total_rewards, scores_eval)
self.record_summary(t)
# compute reward statistics for this batch and log
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
if self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
self.logger.info("- Training done.")
export_plot(
scores_eval,
"Score",
config.env_name,
self.config.plot_output)
def train(self, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
self.init_averages()
t = last_eval = curri_idx = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
prog = Progbar(target=self.config.nsteps_train)
rcopy = RepeatCopy(num_bits=self.config.num_bits,
batch_size=self.config.batch_size,
min_length=self.config.min_length,
max_length=self.config.max_length,
min_repeats=self.config.min_repeats,
max_repeats=self.config.max_repeats)
# interact with environment
while t < self.config.nsteps_train:
t += 1
last_eval += 1
config = self.config
batch_data = rcopy()
# perform a training step
loss_eval, grad_eval = self.train_step(t, lr_schedule.epsilon,
batch_data)
# logging stuff
if ((t % config.log_freq == 0)
and (t % config.learning_freq == 0)):
self.update_averages(scores_eval)
lr_schedule.update(t)
prog.update(t + 1,
exact=[("Loss", loss_eval), ("Grads", grad_eval),
("lr", lr_schedule.epsilon)])
if t >= config.nsteps_train:
break
if last_eval >= config.eval_freq:
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d" % (t))
scores_eval += [self.evaluate()]
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self):
self.baseline = -1000.0
for t in range(self.num_iterations):
#print ('iterations:', t)
actions, con_actions, returns, losses = self.sample_model_reward_return(
t)
returns = returns * 2
#self.baseline = (t*self.baseline + np.mean(returns)) / (t+1)
if self.baseline == -1000.0:
self.baseline = np.mean(returns)
else:
self.baseline = 0.6 * self.baseline + 0.4 * np.mean(returns)
self.sess.run(self.train_op,
feed_dict={
self.action_placeholder: actions,
self.con_action_placeholder: con_actions,
self.advantage_placeholder:
returns - self.baseline
})
avg_acc = np.mean(returns)
used = len(self._used_models)
self._num_used_models.append(used)
self.log_acc.append(avg_acc)
#sigma_reward = np.sqrt(np.var(returns) / len(total_rewards))
msg = "Average accuracy within a batch: {:04.2f}".format(avg_acc)
self.logger.info(msg)
#print (actions)
self.logger.info("- Training done.")
export_plot(self.log_acc, "Score", 'NAS-CNN', "./batch_accuracy.png")
export_plot(self._num_used_models, "Number of distinct models sampled",
'NAS-CNN', "./used_models.png")
def train(self):
for t in range(self.num_batches):
actions, returns = self.sample_model_reward_return()
#self.baseline = (t*self.baseline + np.mean(returns)) / (t+1)
if self.baseline == -1000.0:
self.baseline = np.mean(returns)
else:
self.baseline = 0.6 * self.baseline + 0.4 * np.mean(returns)
self.sess.run(self.train_op,
feed_dict={
self.action_placeholder: actions,
self.advantage_placeholder: returns
}) # not using baseline here
avg_acc = np.mean(returns)
#calculate number of used models:
used = 0
#for key in self._used_dict.keys():
#used += self._used_dict[key]
used = np.sum(self._used_dict)
self._num_used_models.append(used)
self.log_acc.append(avg_acc)
#sigma_reward = np.sqrt(np.var(returns) / len(total_rewards))
msg = "Average accuracy within a batch: {:04.2f}".format(avg_acc)
self.logger.info(msg)
print(actions)
self.logger.info("- Training done.")
#export_plot(self.log_acc, "Batch_Accuracy", 'NAS-DNN', "./batch_accuracy.png", self._num_used_models, "Sampled Model")
export_plot(self.log_acc, "Score", 'NAS-DNN', "./batch_accuracy.png")
export_plot(self._num_used_models, "Models Sampled", 'NAS-DNN',
"./used_models.png")
def train(self, model_a, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size,
self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
#scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
self.env.state.is_render_image = self.config.render_train
orientation_map = [
np.array([0, 1]),
np.array([-1, 0]),
np.array([0, -1]),
np.array([1, 0])
]
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
flag = True
while flag:
state = self.env.reset() # h x w x c
agent_location = self.env.state.agent_location
if self.env.teacher.dist_map[agent_location[1],
agent_location[0]] != np.inf:
flag = False
model_a.env.reset()
model_a.env.state.copy_state(model_a.env.agent, self.env.state)
h_state = (np.zeros([1, self.config.h_size]),
np.zeros([1, self.config.h_size]))
h_state_a = (np.zeros([1, model_a.config.h_size]),
np.zeros([1, model_a.config.h_size]))
slen = np.ones(1).astype('int32')
action = 0
for i in range(200):
t += 1
last_eval += 1
last_record += 1
raw_goal_state, goal_state = self.convert_state_to_goal_state(
state)
#### for replay_buffer
# replay memory stuff
idx = replay_buffer.store_frame(raw_goal_state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
best_action, q_values, h_state = self.get_best_action(
[q_input], h_state, slen, [action])
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
reward = 0
#### perform action in env ####
#### update goal obs image ####
if action == 1:
if self.config.render_train:
self.env.teacher.update_goal_obs_image(self.env.state)
if self.config.render_train: self.env.render()
#### teacher move ####
agent_location = self.env.state.agent_location
agent_orientation = self.env.state.agent_orientation
goal_location = agent_location + agent_orientation
gt_action = self.env.teacher.action_map[agent_location[1],
agent_location[0]]
if np.dot(agent_orientation, orientation_map[gt_action]) == 1:
new_state, reward_i, done = self.env.step(0)
else:
tmp = np.cross(agent_orientation,
orientation_map[gt_action])
if tmp == 1:
new_state, reward_i, done = self.env.step(3)
else:
new_state, reward_i, done = self.env.step(2)
#### issue command ####
if action == 1:
model_a.env.teacher.set_goal(goal_state, goal_location)
reward_a = model_a.navi_goal(h_state_a, goal_state)
if model_a.env.teacher.goal_finish:
reward += reward_i
reward += reward_a
reward += -1
self.env.state.teleport(
self.env.agent, model_a.env.state.agent_location,
model_a.env.state.agent_orientation)
new_state = self.env.state.onehot_state
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
state = new_state
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer,
lr_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1,
exact=[("Loss", loss_eval),
("Avg R", self.avg_reward),
("Max R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval),
("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d" % (t))
scores_eval += [self.evaluate(model_a)]
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate(model_a)]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, exp_schedule, lr_schedule, exp_schedule1, env=None):
"""
Performs training of Q only on agent 0
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
if env is None:
env = self.env
# initialize replay buffer and variables
rewards = deque(maxlen=self.config.num_episodes_test)
rewardsB = deque(maxlen=self.config.num_episodes_test)
self.model_0.rewards = rewards
self.model_1.rewards = rewardsB
# self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
self.model_0.train_init()
self.model_1.train_init()
# next_fire_B = False
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset()
# need_new_ball = False
while True:
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: env.render()
action_0 = self.model_0.train_step_pre(state, exp_schedule)
# if exp_schedule.epsilon == 1:
# action_1 = exp_schedule.get_action(0,3) # agent altogether
# else:
action_1 = self.model_1.train_step_pre(state[:, ::-1],
exp_schedule1)
cur_action = actions.trans(action_0, action_1)
# perform action in env
new_state, reward, done, info = env.step(cur_action)
# print("Reward", reward)
# Problem
loss_e0, grad_e0 = self.model_0.train_step_post(
reward, done, t, lr_schedule, True)
self.model_1.train_step_post(-reward, done, t, lr_schedule,
False)
state = new_state
# logging stuff
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
# self.update_averages(rewards, max_q_values, q_values, scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1,
exact=[
("Loss", loss_e0),
("Avg R", np.mean(rewards)),
("Max R", np.max(rewards)),
("Min R", np.min(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_e0),
("Max Q",
np.mean(self.model_0.max_q_values)),
("lr", lr_schedule.epsilon)
])
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
rewardsB.append(-total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
scores_eval += [self.evaluate()]
if (t > self.config.learning_start) and self.config.record and (
last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record(exp_schedule)
self.model_0.save(t) # save the models
self.model_1.save(t) # save the models
# last words
self.logger.info("- Training done.")
self.model_0.save() # save the models
self.model_1.save() # save the models
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size,
self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
last_frames = deque(maxlen=4)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += []
extractor = PongExtractor()
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset()
last_frame = state
last_frames.append(state)
while True:
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: self.env.render()
feats = extractor.extract(np.squeeze(state))
# replay memory stuff
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
best_action, q_values = self.get_best_action(q_input)
embedding = self.sess.run(self.hidden,
feed_dict={self.s: [q_input]})[0]
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
if t % 100 == 0:
# print state.shape
# frame = np.zeros(np.squeeze(state).shape)
# for f in last_frames:
# frame = frame + np.squeeze(f)
# frame = frame / len(last_frames)
frame = np.squeeze(state)
last_frame = np.squeeze(last_frame)
pickle.dump(
last_frames,
open('frames/embedding/atari{}.p'.format(t), 'w'))
for i in range(4):
f = np.squeeze(last_frames[i])
scipy.misc.imsave(
'frames/embedding/atari{}.png'.format(t - 3 + i),
f)
# scipy.misc.imsave('frames/atari{}.png'.format(t-1),last_frame)
# posfile = open('frames/atari{}.txt'.format(t),'w')
# posfile.write('Opp Paddle:\t{}\n'.format(oppY))
# posfile.write('Player Paddle:\t{}\n'.format(playerY))
# posfile.write('ball x:\t{}\n'.format(ballX))
# posfile.write('ball y:\t{}\n'.format(ballY))
# posfile.close()
np.savetxt('frames/embedding/pong{}.txt'.format(t),
feats,
fmt='%.2f')
# perform action in env
new_state, reward, done, info = self.env.step(action)
# print "state shape:",state.shape()
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
last_frame = state
state = new_state
last_frames.append(state)
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer,
lr_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1,
exact=[("Loss", loss_eval),
("Avg R", self.avg_reward),
("Max R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval),
("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
scores_eval += [self.evaluate()]
if (t > self.config.learning_start) and self.config.record and (
last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
# last words
self.logger.info("- Training done.")
self.save()
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, model_i, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
self.init_averages()
t = last_eval = curri_idx = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
while t < self.config.nsteps_train:
t += 1
last_eval += 1
encoding_batch = []
predflag_batch = []
target_action_batch = []
slen_batch = []
max_len = 0
for i in range(self.config.batch_size):
#config = self.config
#config.n_node, config.k_ring, config.p_rewiring, config.path_len_limit, config.planning_len = cr_schedule[curri_idx]
#self.env.reset(config) # h x w x c
encoding, target_action, predflag = model_i.gen_sample_seq(self.config.ndigits, self.config.nway)
encoding_batch.append(encoding[None])
predflag_batch.append(predflag[None])
target_action_batch.append(target_action[None])
slen_batch.append(encoding.shape[0])
if encoding.shape[0]>max_len:
max_len = encoding.shape[0]
batch_data = DatasetTensors(np.concatenate([np.concatenate([x, np.zeros([1, max_len-x.shape[1], x.shape[2]])], axis=1) for x in encoding_batch], axis=0),
np.concatenate([np.concatenate([x, np.zeros([1, max_len-x.shape[1], x.shape[2]])], axis=1) for x in target_action_batch], axis=0),
np.concatenate([np.concatenate([x, np.zeros([1, max_len-x.shape[1]])], axis=1) for x in predflag_batch], axis=0), np.array(slen_batch).astype('int32'))
# perform a training step
loss_eval, grad_eval = self.train_step(t, lr_schedule.epsilon, batch_data)
# logging stuff
if ((t % self.config.log_freq == 0) and (t % self.config.learning_freq == 0)):
self.update_averages(scores_eval)
lr_schedule.update(t)
prog.update(t + 1, exact=[("Loss", loss_eval), ("Grads", grad_eval), ("lr", lr_schedule.epsilon)])
if t >= self.config.nsteps_train:
break
if last_eval >= self.config.eval_freq:
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d"%(t))
scores_eval += [self.evaluate(model_i)]
'''
if scores_eval[-1]>0.8:
curri_idx += 1
msg = "Upgrade to lesson {:d}".format(int(curri_idx))
self.logger.info(msg)
self.logger.info("----------Start Computing Final Score----------")
scores_eval += [self.evaluate(model_i)]
self.logger.info("----------Finish Computing Final Score----------")
'''
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate(cr_schedule)]
export_plot(scores_eval, "Scores", self.config.plot_output)
def export_score(self, scores_eval):
export_plot(
scores_eval, "Scores",
self.config.plot_output + "scores_" + str(self.index) + ".png")
def train(self, model_a, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size, self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
#scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
self.env.state.is_render_image = self.config.render_train
model_a.env.state.is_render_image = model_a.config.render_train
orientation_map = [np.array([0, 1]), np.array([-1, 0]), np.array([0, -1]), np.array([1, 0])]
npath = self.config.npath # paths to generate in each environment
nquery = self.config.nquery # query to generate in each environment
max_plan_len = self.config.max_plan_len
ndigits = self.config.ndigits
nway = self.config.nway
num_classes = len(self.env.state.xmap.item_class_id)
# three steps:
# 1. sample paths from the teacher environment and pass to dnc
# 2. get immediate reward from whether agent could reach the subgoal
# 3. sample query paths and ask agent to follow the plan, get the final big reward
# -- train one step after each teacher's move
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
self.env.reset()
model_a.env.reset()
model_a.env.state.copy_state(model_a.env.agent, self.env.state)
dnc_state = DNC.zero_state(self.config, batch_size=1)
h_state = (np.zeros([1,self.config.h_size]),np.zeros([1,self.config.h_size]))
slen = np.ones(1).astype('int32')
action = 0
# sample paths
for i in range(npath):
state_seq, path_loc, path_ori = self.env.teacher.gen_sample_seq(self.env.state)
state_seq_encoding = DRQN_planner.encode_state(state_seq, ndigits, nway)
goal_state_seq = np.reshape(state_seq, [state_seq.shape[0], 4, 3, 3, num_classes+2]).astype('bool')
#### missing could be everything ####
goal_state_seq = np.tile(goal_state_seq[:,:,:,:,[num_classes]], [1,1,1,1,num_classes+2])+goal_state_seq
#### treat missing observation as correct observation ####
goal_state_seq[:,:,:,:,num_classes] = True
#### transpose
goal_state_seq = np.transpose(goal_state_seq, [0,2,3,4,1])
path_len = state_seq.shape[0]
mask_seq = np.logical_not(state_seq[:,:3,:,num_classes])
flag_seq = np.zeros([path_len])
flag_seq[-1] = 1
model_a.env.state.teleport(model_a.env.agent, path_loc[0], orientation_map[path_ori[0]])
for j in range(path_len):
# get agate from dnc
cur_dnc_in = np.concatenate([state_seq_encoding[j].reshape(-1),mask_seq[j].reshape(-1), np.array([0, flag_seq[j]])], axis=0)
agate_dnc_val = self.sess.run(self.agate_dnc, feed_dict={self.s_dnc: cur_dnc_in[None], self.hs_dnc: dnc_state})
agate_dnc_val = agate_dnc_val[0,0]
# get q value and sample action
idx = replay_buffer.store_frame(state_seq[j])
q_input = replay_buffer.encode_recent_observation()
best_action, q_values, h_state = self.get_best_action([q_input], h_state, slen, [action], [agate_dnc_val])
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# take action and update dnc
cur_dnc_in[-2] = action
dnc_state = self.sess.run(self.hs_out_dnc, feed_dict={self.s_dnc: cur_dnc_in[None], self.hs_dnc: dnc_state})
# acquire reward
reward = 0
done = False
if action==1:
h_state_a = (np.zeros([1,model_a.config.h_size]),np.zeros([1,model_a.config.h_size]))
model_a.env.teacher.set_goal(goal_state_seq[j], path_loc[j])
reward_a = model_a.navi_goal(h_state_a, goal_state_seq[j])
if not model_a.env.teacher.goal_finish:
reward += -0.05
reward += -0.05
model_a.env.state.teleport(model_a.env.agent, path_loc[j], orientation_map[path_ori[j]])
# acquire final reward
if i==npath-1 and j==path_len-1:
done = True
reward_list = list()
for k in range(nquery):
reward_list.append(0)
src_inputs, tgt_inputs, src_loc, tgt_loc, goal_obs_onehot_state = self.env.teacher.gen_sample_query(self.env.state)
src_inputs = DRQN_planner.encode_state(src_inputs, ndigits, nway)
tgt_inputs = DRQN_planner.encode_state(tgt_inputs, ndigits, nway)
path_dnc_val, target_ldm_dnc_val = self.sess.run([self.path_dnc, self.target_ldm_dnc], feed_dict={self.hs_dnc: dnc_state, self.src_inputs_dnc: src_inputs[None],
self.tgt_inputs_dnc: tgt_inputs[None], self.max_len_dnc: max_plan_len})
path_dnc_val = DRQN_planner.decode_state(np.reshape(path_dnc_val[0], [max_plan_len, 3, 3, -1]), ndigits, nway, num_classes+2)
target_ldm_dnc_val = DRQN_planner.decode_state(np.reshape(target_ldm_dnc_val[0], [3, 3, -1]), ndigits, nway, num_classes+2)
path_dnc_val_inner = np.argmax(path_dnc_val, axis=3)
target_ldm_dnc_val_inner = np.argmax(target_ldm_dnc_val, axis=2)
cur_len = max_plan_len
for l in range(max_plan_len):
if (path_dnc_val_inner[l]==target_ldm_dnc_val_inner).all():
cur_len = l+1
break
path_dnc_val = path_dnc_val[:cur_len]
path_dnc_val = np.concatenate([path_dnc_val, goal_obs_onehot_state[None]], 0)
#### modify goal state ####
#### missing could be everything ####
path_dnc_val = np.tile(path_dnc_val[:,:,:,[num_classes]], [1,1,1,num_classes+2])+path_dnc_val
#### treat missing observation as correct observation ####
path_dnc_val[:,:,:,num_classes] = True
model_a.env.state.teleport(model_a.env.agent, src_loc, np.array([0,1]))
h_state_a = (np.zeros([1,model_a.config.h_size]),np.zeros([1,model_a.config.h_size]))
for l in range(path_dnc_val.shape[0]):
cur_goal_state = path_dnc_val[l]
cur_goal_state = np.expand_dims(cur_goal_state, 3)
cur_goal_state = np.concatenate([np.rot90(cur_goal_state, 0), np.rot90(cur_goal_state, 1),
np.rot90(cur_goal_state, 2), np.rot90(cur_goal_state, 3)], 3)
model_a.env.teacher.set_goal(cur_goal_state, tgt_loc)
reward_list[-1] += model_a.navi_goal(h_state_a, cur_goal_state)
if model_a.env.teacher.goal_finish:
reward_list[-1] += 10
reward += sum(reward_list)/len(reward_list)
# store everything into replay buffer
replay_buffer.store_effect(idx, action, agate_dnc_val, reward, done)
t += 1
last_eval += 1
last_record += 1
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer, lr_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start) and (t % self.config.log_freq == 0) and
(t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values, scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1, exact=[("Loss", loss_eval), ("Avg R", self.avg_reward),
("Max R", np.max(rewards)), ("eps", exp_schedule.epsilon),
("Grads", grad_eval), ("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(t,
self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval > self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d"%(t))
scores_eval += [self.evaluate(model_a)]
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate(model_a)]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self):
"""
Performs training
You do not have to change or use anything here, but take a look
to see how all the code you've written fits together!
"""
last_eval = 0
last_record = 0
scores_eval = []
self.init_averages()
scores_eval = [] # list of scores computed at iteration time
previous_avg_reward = -np.Inf
for t in range(self.config.num_batches):
# collect a minibatch of samples
paths, total_rewards = self.sample_path(self.env)
scores_eval = scores_eval + total_rewards
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
# compute Q-val estimates (discounted future returns) for each time step
returns = self.get_returns(paths)
advantages = self.calculate_advantage(returns, observations)
# run training operations
if self.config.use_baseline:
self.update_baseline(returns, observations)
if self.config.use_sgd is True:
print("USE SGD")
feed_dict = {
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.sgd_lr_placeholder: self.sgd_lr,
self.advantage_placeholder: advantages
}
old_params = self.sess.run(self.get_pi_params)
# 1 time is enough even if policy is stochastic: checks ok ... results are very consistent
old_actions = self.sess.run(
self.sampled_action,
feed_dict={self.observation_placeholder: observations})
start = timer()
old_penalty = env.penalty(observations, old_actions)
end = timer() # Takes 17 seconds on my setup
print("...Time to compute penalty: {}".format(end - start))
print("...len: {} {} {}".format(len(old_params),
len(observations),
len(old_actions)))
print("...old_penalty {}".format(old_penalty))
sgd_gradient = self.sess.run(
self.gradient, feed_dict) # !!! DO NOT SWAP LINES !!!
self.sess.run(self.sgd_train_op, feed_dict)
new_actions = self.sess.run(
self.sampled_action,
feed_dict={self.observation_placeholder: observations})
sgd_penalty = env.penalty(observations, new_actions)
print("...sgd_penalty {}".format(sgd_penalty))
sgd_params = self.sess.run(self.get_pi_params)
xxx_params = old_params - self.sgd_lr * sgd_gradient
print(np.linalg.norm(xxx_params - sgd_params, ord=2))
#assert 2==1 # checks ongoing
if sgd_penalty > old_penalty:
start = timer()
backtrack_lr = copy.copy(self.sgd_lr)
for i in range(self.config.backtrack_iters):
backtrack_lr *= self.config.backtrack_decay
self.sess.run(self.set_pi_params,
feed_dict={
self.v_ph:
old_params -
backtrack_lr * sgd_gradient
})
new_actions = self.sess.run(
self.sampled_action,
feed_dict={
self.observation_placeholder: observations
})
bt_penalty = env.penalty(observations, new_actions)
print(
"...BACKTRACKING bt_penalty {}".format(bt_penalty))
if bt_penalty < sgd_penalty:
print(
"BACKTRACKING: improvement at iter {} bt_penalty={} sgd_penalty={}"
.format(i, bt_penalty, sgd_penalty))
break
if i == self.config.backtrack_iters - 1:
# Nothing better found during backtracking, restore sgd_params
self.sess.run(self.set_pi_params,
feed_dict={self.v_ph: sgd_params})
end = timer()
print("...Backtracking Time: {}".format(end - start))
else:
print("USE ADAM")
self.sess.run(self.train_op,
feed_dict={
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.advantage_placeholder: advantages
})
# tf stuff
if (t % self.config.summary_freq == 0):
self.update_averages(total_rewards, scores_eval)
self.record_summary(t)
# compute reward statistics for this batch and log
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
#if self.config.use_sgd is True and avg_reward < previous_avg_reward:
# self.sgd_lr = self.sgd_lr * 0.9
# print("Decay SGD LR to {}".format(self.sgd_lr))
#previous_avg_reward = avg_reward
if self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
self.logger.info("- Training done.")
export_plot(scores_eval, "Score", config.env_name,
self.config.plot_output)
def train(self, beta_schedule, lr_schedule, cr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
self.init_averages()
t = last_eval = curri_idx = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
curriculum_batch_size = np.ceil(
self.config.nsteps_train /
cr_schedule.n_curriculum).astype('int32')
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
while t < self.config.nsteps_train:
t += 1
last_eval += 1
config = self.config
config.n_node, config.k_ring, config.p_rewiring, config.path_len_limit = cr_schedule[
curri_idx]
self.env.reset(config) # h x w x c
h_state = DNC.zero_state(config, batch_size=1)
encoding, predflag, target_action = self.env.prepare_seq()
slen = np.array(encoding.shape[0]).astype('int32')
# describe graph, query and planning
h_state = self.sess.run(self.hs_out,
feed_dict={
self.s: encoding[None],
self.hs: h_state,
self.slen: slen
})
past_state = -1
past_action_onehot = -1
encoding_a = np.zeros([config.max_step_len, encoding.shape[1]])
predflag_a = np.zeros(config.max_step_len)
target_action_a = np.zeros(
[config.max_step_len, target_action.shape[1]])
for i in range(config.max_step_len):
current_encoding = GraphWorld.convert_triplets_to_encoding(
np.array([[
past_state, self.env.current_state, past_action_onehot
]]).astype('int32'), config.ndigits, config.nway)
current_encoding = np.concatenate(
[current_encoding, np.array([[1, 0]])], axis=1)
pred_action, h_state = self.sess.run(
[self.q, self.hs_out],
feed_dict={
self.s: current_encoding[None],
self.hs: h_state,
self.slen: np.ones(1).astype('int32')
})
gt_action = self.env.get_gt_action()
action = self.get_action(pred_action[0], gt_action,
beta_schedule.epsilon)
past_state = self.env.current_state
_, done, past_action_onehot = self.env.step(action)
encoding_a[i, :] = current_encoding[0]
predflag_a[i] = 1
target_action_a[i] = gt_action
slen += 1
if done:
break
batch_data = (np.concatenate([encoding, encoding_a], axis=0)[None],
np.concatenate([predflag, predflag_a], axis=0),
np.concatenate([target_action, target_action_a],
axis=0), slen)
# perform a training step
loss_eval, grad_eval = self.train_step(t, lr_schedule.epsilon,
batch_data)
# logging stuff
if ((t % config.log_freq == 0)
and (t % config.learning_freq == 0)):
self.update_averages(scores_eval)
beta_schedule.update(t)
lr_schedule.update(t)
prog.update(t + 1,
exact=[("Loss", loss_eval), ("Grads", grad_eval),
("lr", lr_schedule.epsilon)])
if t >= config.nsteps_train:
break
if last_eval >= config.eval_freq:
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d" % (t))
scores_eval += [self.evaluate(cr_schedule, curri_idx)]
if scores_eval[-1] > 0.8:
curri_idx += 1
msg = "Upgrade to lesson {:d}".format(int(curri_idx))
self.logger.info(msg)
self.logger.info(
"----------Start Computing Final Score----------")
scores_eval += [self.evaluate(cr_schedule)]
self.logger.info(
"----------Finish Computing Final Score----------")
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate(cr_schedule)]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
# Initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.FLAGS.buffer_size,
self.FLAGS.state_hist)
rewards = deque(maxlen=self.FLAGS.num_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = 0 # time control of nb of steps
loss_eval = grad_eval = 0
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate(self.env, self.FLAGS.num_test)]
self.prog = Progbar(target=self.FLAGS.train_steps)
# Train for # of train steps
while t < self.FLAGS.train_steps:
continual_crash = 0
try:
total_reward = 0
ep_len = 0
state = self.env.reset()
# Run for 1 episode and update the buffer
while True:
ep_len += 1
# replay memory stuff
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
best_action, q_values = self.network.get_best_action(
q_input)
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, reward, done, info = self.env.step(action)
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
state = new_state
# Count reward
total_reward += reward
# Stop at end of episode
if done: break
#Store episodic rewards
if ep_len > 1: rewards.append(total_reward)
# Learn using replay
while True:
t += 1
ep_len -= 1
# Make train step if necessary
if ((t > self.FLAGS.learn_start)
and (t % self.FLAGS.learn_every == 0)):
loss_eval, grad_eval = self.network.update_step(
t, replay_buffer, lr_schedule.epsilon,
self.summary)
exp_schedule.update(t)
lr_schedule.update(t)
if (t % self.FLAGS.target_every == 0):
self.network.update_target_params()
# Update logs if necessary
if ((t > self.FLAGS.learn_start)
and (t % self.FLAGS.log_every == 0)
and (len(rewards) > 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
self.update_logs(t, loss_eval, rewards,
exp_schedule.epsilon, grad_eval,
lr_schedule.epsilon)
# Update logs if necessary
elif (t < self.FLAGS.learn_start) and (
t % self.FLAGS.log_every == 0):
sys.stdout.write(
"\rPopulating the memory {}/{}...".format(
t, self.FLAGS.learn_start))
sys.stdout.flush()
if ((t > self.FLAGS.learn_start)
and (t % self.FLAGS.check_every == 0)):
# Evaluate current model
scores_eval += [
self.evaluate(self.env, self.FLAGS.num_test)
]
# Save current Model
self.network.save()
# Record video of current model
if self.FLAGS.record:
self.record()
if ep_len <= 0 or t >= self.FLAGS.train_steps: break
continual_crash = 0
except Exception as e:
continual_crash += 1
self.logger.info(e)
if continual_crash >= 10:
self.logger.info("Crashed 10 times -- stopping u suck")
raise e
else:
t -= 1
self.logger.info("Env crash, making new env")
time.sleep(60)
self.env = create_slither_env(self.FLAGS.state_type)
self.env = Unvectorize(self.env)
self.env.configure(fps=self.FLAGS.fps,
remotes=self.FLAGS.remotes,
start_timeout=15 * 60,
vnc_driver='go',
vnc_kwargs={
'encoding': 'tight',
'compress_level': 0,
'fine_quality_level': 50
})
time.sleep(60)
# End of training
self.logger.info("- Training done.")
self.network.save()
scores_eval += [self.evaluate(self.env, self.FLAGS.num_test)]
export_plot(scores_eval, "Scores", self.FLAGS.plot_path)
def train(self):
last_record = 0
self.init_averages()
scores_eval = []
self.plot = {
'room' + str(i): {j: []
for j in range(config.num_sub_policies)}
for i in range(4)
}
for t in range(self.config.num_batches):
# print(t, self.get_epsilon(t))
paths, total_rewards = self.sample_path(env=self.env)
scores_eval += total_rewards
if str(config.env_name).startswith("Fourrooms"):
observations = np.expand_dims(
np.concatenate([path["observation"] for path in paths]),
axis=1)
else:
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
returns = self.get_returns(paths)
advantages = self.calculate_advantage(returns, observations)
if self.config.use_baseline:
self.update_baseline(returns, observations)
self.sess.run(
self.train_op,
feed_dict={
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.advantage_placeholder: advantages
})
if t % self.config.summary_freq == 0:
self.update_averages(total_rewards, scores_eval)
self.record_summary(self.batch_counter)
self.batch_counter = self.batch_counter + 1
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
last_record += 1
if self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
if t % config.record_freq == 0:
self.save_model_checkpoint(self.sess, self.saver,
os.path.join(
self.config.output_path,
'model.ckpt'), t)
self.logger.info("- Training done.")
export_plot(scores_eval, "Score", config.env_name,
self.config.plot_output)
if str(config.env_name).startswith(
"Fourrooms") and config.examine_master:
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [12, 12]
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
2, 2, sharex='col', sharey='row')
axes = {'room0': ax1, 'room1': ax2, 'room2': ax3, 'room3': ax4}
for room in self.plot:
axes[room].set_title(room, size=20)
for sub in range(config.num_sub_policies):
prob_list = self.plot[room][sub]
axes[room].plot(
range(len(prob_list)), prob_list, linewidth=5)
axes[room].legend(
[
'subpolicy' + str(sub)
for sub in range(config.num_sub_policies)
],
loc='upper left',
prop={
'size': 20
})
plt.tight_layout()
plt.savefig('Rooms and Subs', dpi=300)
def train(self, exp_schedule, lr_schedule, choose_teacher_strategy=None):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
replay_buffer = ReplayBuffer(self.config.buffer_size,
self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
allsteps = []
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset()
while True:
if self.config.state_subspace is not None:
out_of_bounds = False
if self.config.state_subspace in [
'ball_top_half', 'ball_bottom_half'
]:
image = self.env.unwrapped._get_obs()
ball_position = ball_half_screen_position(image)
# check if ball is in top half but we're restricted to bottom half
if ball_position == 1 and self.config.state_subspace == 'ball_bottom_half':
out_of_bounds = True
# check if ball is in bottom half but we're restricted to top half
elif ball_position == 0 and self.config.state_subspace == 'ball_top_half':
out_of_bounds = True
else:
raise NotImplementedError
if out_of_bounds: # current state is outside of this agent's state subspace
# perform action in env
state, reward, done, info = self.env.step(action)
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: self.env.render()
# replay memory stuff
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
# self.q_inputs.append(q_input)
# chose action according to current Q and exploration
best_action, q_values = self.get_best_action(q_input)
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, reward, done, info = self.env.step(action)
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
state = new_state
if choose_teacher_strategy is not None:
# store the reward with the teacher choice strategy
choose_teacher_strategy.store_reward(reward, q_input)
# perform a training step
loss_eval, grad_eval = self.train_step(
t, replay_buffer, lr_schedule.epsilon,
choose_teacher_strategy)
# logging stuff
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if choose_teacher_strategy is not None:
choose_teacher_strategy.update_schedule(t)
if len(rewards) > 0:
exact = [("Loss", loss_eval),
("Avg R", self.avg_reward),
("Max R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval), ("Max Q", self.max_q),
("lr", lr_schedule.epsilon)]
if choose_teacher_strategy is not None and hasattr(
choose_teacher_strategy, 'eps_schedule'):
exact.append(
("Choose teacher eps",
choose_teacher_strategy.eps_schedule.epsilon))
prog.update(t + 1, exact=exact)
elif ((t > self.config.learning_start)
and (t % self.config.save_teacher_choice_freq == 0)
and (choose_teacher_strategy is not None)):
choose_teacher_strategy.save(
self.config.teacher_choice_output_path)
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
scores_eval += [self.evaluate()]
if (t > self.config.learning_start) and self.config.record and (
last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
# last words
self.logger.info("- Training done.")
self.save()
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
if choose_teacher_strategy is not None:
choose_teacher_strategy.save(
self.config.teacher_choice_output_path)
def train(self):
"""
Performs training
You do not have to change or use anything here, but take a look
to see how all the code you've written fits together!
"""
last_eval = 0
last_record = 0
self.init_averages()
scores_eval = [] # list of scores computed at iteration time
episode = 0
episode_reward = 0
step = 0
episode_rewards = []
paths = []
observation = self.env.reset()
observation = np.tile(observation, (self.config.batch_size, 1))
self.config.batch_size
for t in range(self.config.num_batches*self.config.batch_size):
if self.discrete:
actions = np.arange(self.action_dim).astype(float)[:, None]
actions = np.reshape(actions, (1, self.config.batch_size))
obs = np.tile(observation, (self.action_dim, 1))
self.sess.run(self.train_op, feed_dict={
self.observation_placeholder : obs,
self.action_placeholder : actions})
for i in range(observation.shape[0]):
action = self.sess.run(self.sampled_actions, feed_dict={self.observation_placeholder : observation[None]})[0]
next_observation, reward, done, info = env.step(action)
next_action = self.sess.run(self.target_sampled_actions, feed_dict={self.next_observation_placeholder : next_observation[None]})[0]
episode_reward += reward
step += 1
action = np.array([action])[None]
next_action = np.array([next_action])[None]
reward = np.array([reward])[None]
done = np.array([done])[None]
self.update_critic(action, next_action, observation[None], next_observation[None], reward, done)
if (t > 0 and t % self.config.update_critic_freq == 0):
self.sess.run(self.update_target_op, feed_dict={})
if (done or step == self.config.max_ep_len-1):
episode_rewards.append(episode_reward)
observation = self.env.reset()
observation = np.tile(observation, (self.config.batch_size, 1))
episode_reward = 0
episode += 1
step = 0
else:
observation = next_observation
# tf stuff
if (t % (self.config.summary_freq*self.config.batch_size) == 0 and t > 0):
self.update_averages(episode_rewards, scores_eval)
self.record_summary(t)
if (t % self.config.batch_size == 0 and t > 0):
# compute reward statistics for this batch and log
avg_reward = np.mean(episode_rewards)
sigma_reward = np.sqrt(np.var(episode_rewards) / len(episode_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(avg_reward, sigma_reward)
self.logger.info(msg)
scores_eval = scores_eval + episode_rewards
episode_rewards = []
if self.config.record and (last_record > (self.config.record_freq*self.config.batch_size)):
self.logger.info("Recording...")
last_record =0
self.record()
self.logger.info("- Training done.")
export_plot(scores_eval, "Score", config.env_name, self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
# Initialize replay buffer and variables
replay_buffer = ReplayBufferAC(self.FLAGS.buffer_size, self.FLAGS.state_hist)
rewards = deque(maxlen=self.FLAGS.num_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = 0 # time control of nb of steps
loss_eval = grad_eval = 0
scores_eval = [] # list of scores computed at iteration time
#scores_eval += [self.evaluate(self.env, self.FLAGS.num_test)]
self.prog = Progbar(target=self.FLAGS.train_steps)
self.prog2 = Progbar(target=self.FLAGS.train_steps)
# Train for # of train steps
while t < self.FLAGS.train_steps:
total_reward = 0
ep_len = 0
state = self.env.reset()
reward = 0
first = 1
q_input = None
# Run for 1 episode and update the buffer
while True:
ep_len += 1
# replay memory stuff
if first == 1:
first = 0
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
best_action, q_values = self.network.get_best_action(q_input)
action = exp_schedule.get_action(best_action)
orig_val = self.network.calcState(q_input)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, new_reward, done, info = self.env.step(action)
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
new_val = self.network.calcState(q_input)
orig_val = orig_val[0][0]
new_val = new_val[0][0]
print (orig_val, new_reward, done, new_val, ep_len)
if not done: # Non-terminal state.
target = reward + ( self.FLAGS.gamma * new_val)
else:
target = reward + ( self.FLAGS.gamma * new_reward )
best_val = max((orig_val), target)
actor_delta = new_val - orig_val
replay_buffer.store_effect(idx-1, action, new_reward, done, best_val, actor_delta)
state = new_state
if done:
replay_buffer.store_effect(idx, action, 0, done, 0, 0)
# Count reward
total_reward += new_reward
reward=new_reward
# Stop at end of episode
if done: break
old_t = t
temp_ep_len = ep_len
while True:
t += 1
temp_ep_len -= 1
if ((t > self.FLAGS.learn_start) and (t % self.FLAGS.learn_every == 0)):
if replay_buffer.can_sample(self.FLAGS.batch_size) == True:
loss_eval, grad_eval = self.network.update_critic_step(t, replay_buffer, lr_schedule.epsilon, self.summary)
# Update logs if necessary
if ((t > self.FLAGS.learn_start) and (t % self.FLAGS.log_every == 0)):
self.update_logs2(t, loss_eval, rewards, exp_schedule.epsilon, grad_eval, lr_schedule.epsilon)
if temp_ep_len <= 0 or t >= self.FLAGS.train_steps: break
rewards.append(total_reward)
# Learn using replay
while True:
t += 1
ep_len -= 1
# Make train step if necessary
if ((t > self.FLAGS.learn_start) and (t % self.FLAGS.learn_every == 0)):
if replay_buffer.can_sample(self.FLAGS.batch_size) == True:
loss_eval, grad_eval = self.network.update_actor_step(t, replay_buffer, lr_schedule.epsilon, self.summary)
exp_schedule.update(t)
lr_schedule.update(t)
# Update logs if necessary
if ((t > self.FLAGS.learn_start) and (t % self.FLAGS.log_every == 0)):
self.update_averages(rewards, max_q_values, q_values, scores_eval)
self.update_logs(t, loss_eval, rewards, exp_schedule.epsilon, grad_eval, lr_schedule.epsilon)
# Update logs if necessary
elif (t < self.FLAGS.learn_start) and (t % self.FLAGS.log_every == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(t, self.FLAGS.learn_start))
sys.stdout.flush()
if ((t > self.FLAGS.learn_start) and (t % self.FLAGS.check_every == 0)):
# Evaluate current model
scores_eval += [self.evaluate(self.env, self.FLAGS.num_test)]
# Save current Model
self.network.save()
# Record video of current model
if self.FLAGS.record:
self.record()
if ep_len <= 0 or t >= self.FLAGS.train_steps: break
# Update episodic rewards
# End of training
self.logger.info("- Training done.")
self.network.save()
scores_eval += [self.evaluate(self.env, self.FLAGS.num_test)]
export_plot(scores_eval, "Scores", self.FLAGS.plot_path)
def export_score(self, scores_eval):
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self):
"""
Performs training
"""
last_eval = 0
last_record = 0
scores_eval = []
self.init_averages()
scores_eval = [] # list of scores computed at iteration time
# Update learning rate
if self.max_roll_distance > 400.0:
self.learning_rate = pow(self.learning_rate, 0.9)
for t in range(self.config.num_batches):
# collect a minibatch of samples
paths, total_rewards, rollout_distances = self.sample_path()
scores_eval = scores_eval + total_rewards
observations = np.concatenate(
[path["observation"] for path in paths])
actions = np.concatenate([path["action"] for path in paths])
rewards = np.concatenate([path["reward"] for path in paths])
# compute Q-val estimates (discounted future returns) for each time step
returns = self.get_returns(paths)
advantages = self.calculate_advantage(returns, observations)
#Check if current model is best:
if max(rollout_distances) > self.max_max_roll_distance:
print('New best model found! Saving under: ',
self.config.best_model_output)
self.saver.save(self.sess, self.config.best_model_output)
# run training operations
if self.config.use_baseline:
self.update_baseline(returns, observations)
self.sess.run(self.train_op,
feed_dict={
self.observation_placeholder: observations,
self.action_placeholder: actions,
self.advantage_placeholder: advantages,
self.lr: self.learning_rate
})
# tf stuff
if (t % self.config.summary_freq == 0):
self.update_averages(total_rewards, scores_eval,
rollout_distances)
self.record_summary(t)
print("Learning rate:", self.learning_rate)
# compute reward statistics for this batch and log
avg_reward = np.mean(total_rewards)
sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))
msg = "Average reward: {:04.2f} +/- {:04.2f}".format(
avg_reward, sigma_reward)
self.logger.info(msg)
self.saver.save(self.sess, self.config.model_output)
self.logger.info("- Training done.")
export_plot(scores_eval, "Score", config.env_name,
self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
if not self.config.batch:
replay_buffer = ReplayBuffer(
self.config.buffer_size, self.config.state_history
)
else:
self.logger.info(
'Loading replay buffer from {}'.format(self.config.buffer_path)
)
replay_buffer = ReplayBuffer.load(self.config.buffer_path)
self.logger.info(
'Loaded buffer with {} observations and {} in buffer'.format(
len(replay_buffer.obs), replay_buffer.num_in_buffer
)
)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
episode_lengths = deque(maxlen=1000)
max_episode_length = 0
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
if not self.config.batch:
state = self.env.reset()
episode_step = 0
avg_episode_length = (
np.nan if len(episode_lengths) == 0 else np.mean(episode_lengths)
)
while True:
t += 1
episode_step += 1
last_eval += 1
last_record += 1
if self.config.render_train:
self.env.render()
if not self.config.batch:
get_action = functools.partial(
exp_schedule.get_action,
episode_num=len(episode_lengths),
episode_step=episode_step,
avg_episode_length=avg_episode_length
)
state, reward, done, _q_values = self.interact(
replay_buffer, state, get_action
)
else:
reward = 0
done = True
_q_values = [0]
# store q values
max_q_values.append(max(_q_values))
q_values.extend(list(_q_values))
# perform a training step
loss_eval, grad_eval = self.train_step(
t, replay_buffer, lr_schedule.epsilon
)
# logging stuff
learning = (t > self.config.learning_start)
learning_and_loggging = (
learning and
(t % self.config.log_freq == 0) and
(t % self.config.learning_freq == 0)
)
if learning_and_loggging:
self.update_averages(
rewards, max_q_values, q_values,
scores_eval, episode_lengths, max_episode_length
)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
if self.config.batch:
exact = [
("Loss", loss_eval),
("Grads", grad_eval),
("lr", lr_schedule.epsilon),
]
else:
exact = [
("Loss", loss_eval),
("Avg_R", self.avg_reward),
("Max_R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval),
("Max_Q", self.max_q),
("lr", lr_schedule.epsilon),
("avg_ep_len", avg_episode_length)
]
prog.update(t + 1, exact=exact)
elif not learning and (t % self.config.log_freq == 0):
sys.stdout.write(
"\rPopulating the memory {}/{}...".format(
t, self.config.learning_start
)
)
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
episode_lengths.append(episode_step)
if episode_step > max_episode_length:
max_episode_length = episode_step
# retrain the clusters every time the max episode
# length changes
if hasattr(self, 'reset_counts'):
self.reset_counts(
n_clusters=max_episode_length,
states=replay_buffer.get_encoded_states(),
actions=replay_buffer.get_actions()
)
break
# updates to perform at the end of an episode
rewards.append(total_reward)
should_evaluate = (
(t > self.config.learning_start) and
(last_eval > self.config.eval_freq)
)
if should_evaluate:
# evaluate our policy
last_eval = 0
print("")
scores_eval.append(self.evaluate())
should_record = (
(t > self.config.learning_start) and
self.config.record and
(last_record > self.config.record_freq)
)
if should_record:
self.logger.info("Recording...")
last_record = 0
self.record()
# last words
self.logger.info("- Training done.")
self.save()
scores_eval.append(self.evaluate())
export_plot(scores_eval, "Scores", self.config.plot_output)
if not self.config.batch:
# save replay buffer
self.logger.info(
'Saving buffer to {}'.format(self.config.buffer_path)
)
replay_buffer.save(self.config.buffer_path)
def train(self, model_a, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size, self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
#scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
self.env.state.is_render_image = self.config.render_train
model_a.env.state.is_render_image = model_a.config.render_train
orientation_map = [np.array([0, 1]), np.array([-1, 0]), np.array([0, -1]), np.array([1, 0])]
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
flag = True
while flag:
state = self.env.reset() # h x w x c
agent_location = self.env.state.agent_location
if self.env.teacher.dist_map[agent_location[1],agent_location[0]]!=np.inf:
flag = False
model_a.env.reset()
model_a.env.state.copy_state(model_a.env.agent, self.env.state)
h_state_fw = (np.zeros([1,self.config.h_size]),np.zeros([1,self.config.h_size]))
h_state_bw = (np.zeros([1,self.config.h_size]),np.zeros([1,self.config.h_size]))
state_batch = list()
goal_state_batch = list()
goal_obs_image_batch = list()
path_loc = list()
path_ori = list()
done_batch = list()
width, height = self.env.state.xmap.dim['width'], self.env.state.xmap.dim['height']
side_radius = min(self.config.visible_radius_unit_side, max(width - 1, height - 1))
block_size = self.env.state.image_block_size
for i in range(200):
#### teacher rotate ####
agent_location = self.env.state.agent_location
agent_orientation = self.env.state.agent_orientation
goal_location = agent_location+agent_orientation
gt_action = self.env.teacher.action_map[agent_location[1], agent_location[0]]
if np.dot(agent_orientation, orientation_map[gt_action])!=1:
tmp = np.cross(agent_orientation, orientation_map[gt_action])
if tmp==1:
state, reward_i, done = self.env.step(3)
else:
state, reward_i, done = self.env.step(2)
continue
path_loc.append(copy.deepcopy(goal_location))
path_ori.append(copy.deepcopy(agent_orientation))
raw_goal_state, goal_state = self.convert_state_to_goal_state(state)
state_batch.append(raw_goal_state[None][None])
goal_state_batch.append(goal_state)
if self.config.render_train:
goal_obs_image_batch.append(self.env.state.image[:3*block_size, (side_radius-1)*block_size:(side_radius+2)*block_size, :])
state, reward_i, done = self.env.step(0)
done_batch.append(done)
if done:
break
slen = np.array([len(state_batch)]).astype('int32')
state_batch = np.concatenate(state_batch, axis=1)
best_action_batch, q_values_batch, h_state_fw, h_state_bw = self.get_best_action_batch(state_batch, h_state_fw, h_state_bw, slen)
action_batch = exp_schedule.get_action_batch(best_action_batch)
for i in range(q_values_batch.shape[0]):
max_q_values.append(max(q_values_batch[i]))
q_values += list(q_values_batch[i])
reward_batch = list()
for i, action in enumerate(action_batch):
if action==0:
reward_batch.append(0)
else:
if self.config.render_train:
model_a.env.teacher.goal_obs_image = goal_obs_image_batch[i]
h_state_a = (np.zeros([1,model_a.config.h_size]),np.zeros([1,model_a.config.h_size]))
model_a.env.teacher.set_goal(goal_state_batch[i], path_loc[i])
reward_a = model_a.navi_goal(h_state_a, goal_state_batch[i])
if model_a.env.teacher.goal_finish:
reward_batch.append(-0.05)
else:
reward_batch.append(-0.1)
#model_a.env.state.teleport(model_a.env.agent, path_loc[i], path_ori[i])
if action_batch[-1]==1 and model_a.env.teacher.goal_finish:
reward_batch[-1] += 1
else:
if self.config.render_train:
model_a.env.teacher.goal_obs_image = goal_obs_image_batch[-1]
h_state_a = (np.zeros([1,model_a.config.h_size]),np.zeros([1,model_a.config.h_size]))
model_a.env.teacher.set_goal(goal_state_batch[-1], path_loc[-1])
reward_a = model_a.navi_goal(h_state_a, goal_state_batch[-1])
if model_a.env.teacher.goal_finish:
reward_batch[-1] += 1
for i in range(action_batch.shape[0]):
idx = replay_buffer.store_frame(state_batch[0][i])
replay_buffer.store_effect(idx, action_batch[i], reward_batch[i], done_batch[i])
for i in range(action_batch.shape[0]):
t += 1
last_eval += 1
last_record += 1
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer, lr_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start) and (t % self.config.log_freq == 0) and
(t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values, scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1, exact=[("Loss", loss_eval), ("Avg R", self.avg_reward),
("Max R", np.max(rewards)), ("eps", exp_schedule.epsilon),
("Grads", grad_eval), ("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(t,
self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward = sum(reward_batch)
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval > self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d"%(t))
scores_eval += [self.evaluate(model_a)]
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate(model_a)]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
if self.config.use_memory:
replay_buffer = ReplayBuffer(
self.config.buffer_size,
self.config.state_history,
memory_size=self.config.memory_unit_size)
else:
replay_buffer = ReplayBuffer(self.config.buffer_size,
self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate()[0]]
prog = Progbar(target=self.config.nsteps_train)
evaluation_result_list = []
oos_evalution_result_list = []
# interact with environment
prev_time = time.time()
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset()
while True:
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: self.env.render()
# replay memory stuff
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
if self.config.use_memory:
prev_memory = replay_buffer.encode_recent_memory()
best_action, q_values, _, next_memory = self.get_best_action_with_memory(
q_input, prev_memory)
next_memory = np.squeeze(next_memory)
else:
best_action, q_values = self.get_best_action(q_input)
# chose action according to current Q and exploration
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, reward, done, info = self.env.step(action)
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
if self.config.use_memory:
replay_buffer.store_memory(idx, next_memory)
state = new_state
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer,
lr_schedule.epsilon)
# logging stuff
time_log_freq = 1000
if t % time_log_freq == 0:
with open(self.config.output_path + 'time_log.txt',
'a') as of:
of.write('{}\n'.format(time.time() - prev_time))
of.write('\n')
prev_time = time.time()
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1,
exact=[("Loss", loss_eval),
("Avg_R", self.avg_reward),
("Max_R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval),
("Max_Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
score, complete, length = self.evaluate()
if complete > 0:
evaluation_result_list += [(score, complete, length)]
if score > self.config.extended_eval_threshold:
self.logger.info('Extended in-sample evaluation...')
self.evaluate(num_episodes=1000)
for _ in range(10):
self.logger.info(
'Extended out-of-sample evaluation...')
oos_result = self.evaluate(
EnvMaze(n=self.config.maze_size), num_episodes=100)
oos_evalution_result_list += [oos_result]
scores_eval += [score]
if (t > self.config.learning_start) and self.config.record and (
last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record = 0
self.record()
# last words
self.logger.info("- Training done.")
self.save()
scores_eval += [self.evaluate()[0]]
export_plot(scores_eval, "Scores", self.config.plot_output)
return evaluation_result_list, oos_evalution_result_list
def train(self, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size, self.config.state_history, self.config)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset()
while True:
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: self.env.render()
# replay memory stuff
idx = replay_buffer.store_frame(state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
best_action, q_values = self.get_best_action(q_input)
action, explore = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, reward, done, info = self.env.step(action)
# store the transition
replay_buffer.store_effect(idx, action, reward, done, explore)
state = new_state
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer, lr_schedule.epsilon, exp_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start) and (t % self.config.log_freq == 0) and
(t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values, scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1, exact=[("Loss", loss_eval), ("Avg R", self.avg_reward),
("Max R", np.max(rewards)), ("eps", exp_schedule.epsilon),
("Grads", grad_eval), ("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(t,
self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval > self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
scores_eval += [self.evaluate()]
if (t > self.config.learning_start) and self.config.record and (last_record > self.config.record_freq):
self.logger.info("Recording...")
last_record =0
self.record()
# last words
self.logger.info("- Training done.")
self.save()
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
def train(self, exp_schedule, lr_schedule):
"""
Performs training of Q
Args:
exp_schedule: Exploration instance s.t.
exp_schedule.get_action(best_action) returns an action
lr_schedule: Schedule for learning rate
"""
# initialize replay buffer and variables
replay_buffer = ReplayBuffer(self.config.buffer_size,
self.config.state_history)
rewards = deque(maxlen=self.config.num_episodes_test)
max_q_values = deque(maxlen=1000)
q_values = deque(maxlen=1000)
self.init_averages()
t = last_eval = last_record = 0 # time control of nb of steps
scores_eval = [] # list of scores computed at iteration time
#scores_eval += [self.evaluate()]
prog = Progbar(target=self.config.nsteps_train)
self.env.state.is_render_image = self.config.render_train
# interact with environment
while t < self.config.nsteps_train:
total_reward = 0
state = self.env.reset() # h x w x c
goal_state = self.env.teacher.goal_obs_onehot_state # h x w x c
h_state = (np.zeros([1, self.config.h_size]),
np.zeros([1, self.config.h_size]))
slen = np.ones(1).astype('int32')
action = 0
for i in range(200):
t += 1
last_eval += 1
last_record += 1
if self.config.render_train: self.env.render()
#### for replay_buffer
# replay memory stuff
idx = replay_buffer.store_frame(state, goal_state)
q_input = replay_buffer.encode_recent_observation()
# chose action according to current Q and exploration
curr_attention = np.equal(
np.sum(np.equal(q_input, goal_state[None][None][None]), 3),
q_input.shape[3])
best_action, q_values, h_state = self.get_best_action(
[q_input], curr_attention[None], h_state, slen, [action])
#best_action, q_values, h_state = self.get_best_action([q_input], goal_state[None][None], h_state, slen, [action])
action = exp_schedule.get_action(best_action)
# store q values
max_q_values.append(max(q_values))
q_values += list(q_values)
# perform action in env
new_state, reward, done = self.env.step(action)
# store the transition
replay_buffer.store_effect(idx, action, reward, done)
state = new_state
# perform a training step
loss_eval, grad_eval = self.train_step(t, replay_buffer,
lr_schedule.epsilon)
# logging stuff
if ((t > self.config.learning_start)
and (t % self.config.log_freq == 0)
and (t % self.config.learning_freq == 0)):
self.update_averages(rewards, max_q_values, q_values,
scores_eval)
exp_schedule.update(t)
lr_schedule.update(t)
if len(rewards) > 0:
prog.update(t + 1,
exact=[("Loss", loss_eval),
("Avg R", self.avg_reward),
("Max R", np.max(rewards)),
("eps", exp_schedule.epsilon),
("Grads", grad_eval),
("Max Q", self.max_q),
("lr", lr_schedule.epsilon)])
elif (t < self.config.learning_start) and (
t % self.config.log_freq == 0):
sys.stdout.write("\rPopulating the memory {}/{}...".format(
t, self.config.learning_start))
sys.stdout.flush()
# count reward
total_reward += reward
if done or t >= self.config.nsteps_train:
break
# updates to perform at the end of an episode
rewards.append(total_reward)
if (t > self.config.learning_start) and (last_eval >
self.config.eval_freq):
# evaluate our policy
last_eval = 0
print("")
self.logger.info("Global step: %d" % (t))
scores_eval += [self.evaluate()]
# last words
self.logger.info("- Training done.")
self.save(t)
scores_eval += [self.evaluate()]
export_plot(scores_eval, "Scores", self.config.plot_output)
