def train_models(X_train, y_train, batchSize, model, loss_log):
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
return loss_log
def train_net(model, params):
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 100000 # Number of frames to play.
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
min_distance = 10000
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
_, state,_= game_state.frame_step((2))
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
car_distance += 1
# Choose an action.
# if random.random() < epsilon or t < observe:
# action = np.random.randint(0, 3) # random
# else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
reward, new_state, distance = game_state.frame_step(action)
# Experience replay storage.
replay.append((state, action, reward, new_state))
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize) # mot liss gom 64 tuble, moi tuble gom 4 phan tu (S, A, R, S')
# Get training values.
X_train, y_train = process_minibatch2(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(
X_train, y_train, batch_size=batchSize,
nb_epoch=1, verbose=0, callbacks=[history]
)
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1.0/train_frames)
if distance < min_distance:
min_distance = distance
# We died, so update stuff.
if reward == -500:
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Update max.
if car_distance > max_car_distance:
max_car_distance = car_distance
# Time it.
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
# Output some stuff so we can watch.
# print("Max_car_distance: %d at %d\tepsilon %f\t(%d)\tdistance %d\t%f fps" %
# (max_car_distance, t, epsilon, car_distance, distance, fps))
# Reset.
car_distance = 0
start_time = timeit.default_timer()
if t % 10 == 0:
print("Max_car_distance: %d at %d\tepsilon %f\t(%d)\tdistance %d \tmin_distance %d" %
(max_car_distance, t, epsilon, car_distance, distance, min_distance))
# Save the model every 25,000 frames.
if t % 10000 == 0:
model.save_weights('saved-models/' + filename + '-' +
str(t) + '.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
# Log results after we're done all frames.
log_results(filename, data_collect, loss_log)
reward = np.dot(train,weightReadings)
reward = reward.astype(int)
if trainCount > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values by Sarsa 0
X_train, y_train = tv.sarsa0_minibatch(minibatch, model, sarsa0P)
# Train the model on this batch.
history = LossHistory()
model.fit(
X_train, y_train, batch_size=batchSize,
nb_epoch=1, verbose=0, callbacks=[history]
)
loss_log.append(history.losses)
state = new_state
if epsilon > final_epsilon and trainCount > observe:
epsilon -= (1/train_frames)
print (epsilon)
# Save the model every 25,000 frames.
filename = 'train5'
def update_replay(self, reward, new_state, action=None):
if action is None:
action = self.lastAction
# Experience replay storage.
self.replay.append(
(np.copy(self.old_state), action, reward, np.copy(new_state)))
# If we're done observing, start training.
if self.t > self.observe:
# If we've stored enough in our buffer, pop the oldest.
if len(self.replay) > self.buffer:
self.replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(self.replay, self.batchSize)
# Get training values.
X_train, y_train = process_minibatch2(minibatch, self.model,
self.sequence_length,
self.end_value, self.GAMMA)
# Train the model on this batch.
history = LossHistory()
self.model.fit(X_train,
y_train,
batch_size=self.batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
self.loss_log.append(history.losses)
if self.t % self.save_every == 0:
if len(self.data_collect) > 50:
# Save the results to a file so we can graph it later.
learn_f = 'results/command-frames/learn_data-' + self.filename + '.csv'
with open(learn_f, 'w', newline='') as data_dump:
wr = csv.writer(data_dump)
wr.writerows(self.data_collect)
plotting.plot_file(learn_f, 'learn')
if len(self.loss_log) > 500:
loss_f = 'results/command-frames/loss_data-' + self.filename + '.csv'
with open(loss_f, 'w', newline='') as lf:
wr = csv.writer(lf)
for loss_item in self.loss_log:
wr.writerow(loss_item)
plotting.plot_file(loss_f, 'loss')
# Update the starting state with S'.
self.state = new_state
# Decrement epsilon over time.
if self.epsilon > 0.1 and self.t > self.observe:
self.epsilon -= (1.0 / self.train_frames)
# We died, so update stuff.
if reward == -500:
# Log the car's distance at this T.
print([self.t, self.hacker_cmds])
self.data_collect.append([self.t, self.hacker_cmds])
# Update max.
if self.hacker_cmds > self.max_hacker_cmds:
self.max_hacker_cmds = self.hacker_cmds
# Time it.
tot_time = timeit.default_timer() - self.start_time
fps = self.hacker_cmds / tot_time
# Output some stuff so we can watch.
print("Max: %d at %d\tepsilon %f\t(%d)\t%f fps" %
(self.max_hacker_cmds, self.t, self.epsilon,
self.hacker_cmds, fps))
# Reset.
self.hacker_cmds = 0
start_time = timeit.default_timer()
# Save the model every 25,000 frames.
if self.t % self.save_every == 0:
pickle._dump(
self.replay,
open(self.save_replay_file_prefix + "-" + str(self.t), "wb"))
model_save_filename = self.save_model_file_prefix + self.filename + '-' + str(
self.t) + '.h5'
self.model.save_weights(model_save_filename, overwrite=True)
print("Saving model %s - %d" % (self.filename, self.t))
def train(model, params):
filename = params_to_filename(params)
EPISODE = 10
FRAMES = 4000
OBSERVE = FRAMES * 3
epsilon = 1
batchSize = params['batchSize']
buffer = params['buffer']
replay = []
minibatch = []
total_frames = 0
path_log = []
loss_log = []
# min_path_length = 0
for m in range(EPISODE):
print("Episode: %d" % (m))
gameObject = GameClass(draw_screen=True, display_path=True, fps=FPS)
# Choose no action in the initial frame
action = 2
reward, state = gameObject.frame_step(action)
for t in range(FRAMES):
total_frames += 1
if t % (FRAMES / 10) == 0:
print("Frames: %d" % (t))
# Choose the action based on the epsilon greedy algorithm
if (random.random() < epsilon
or total_frames < OBSERVE): # choose random action
action = np.random.randint(0, 3)
else: # choose best action from Q(s,a) values
# Let's run our Q function on (state,action) to get Q values for all possible actions
Q = np.zeros(3)
for a in range(3):
features = get_features(state, a)
Q[a] = model.predict(features, batch_size=batchSize)
action = (np.argmax(Q))
# Execute the action, observe new state and reward
reward, state_new = gameObject.frame_step(action)
path_length = gameObject.num_steps
# Store the (state, action, reward, new state) pair in the replay
memory = state, action, reward, state_new
replay.append(memory)
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory if we have enough samples
if total_frames > OBSERVE:
minibatch = random.sample(replay, batchSize)
# Process the minibatch to get the training data
X_train, y_train = process_minibatch(minibatch, model,
batchSize)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Decrement epsilon over time.
if epsilon > 0.1:
epsilon -= 1.0 / (FRAMES * EPISODE - OBSERVE)
# Update the starting state with S'.
state = state_new
# Stop this episode if we achieved the goal
if gameObject.check_reach_goal():
# Log the robot's path length
path_log.append([m, path_length])
# # Update the min
# if path_length < min_path_length:
# min_path_length = path_length
# # Output some stuff so we can watch.
# print("Min: %d \t epsilon %f\t(%d)" %
# (min_path_length, epsilon, path_length))
# Stop this episode
break
# Save the model every episode after observation.
if total_frames > OBSERVE:
model.save('saved-models/model_nn-' + filename + '-' + str(m) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, m))
# Log results after we're done all episodes.
log_results(filename, path_log, loss_log, m)
def train_net(model, params, mode='grid'):
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 10000 # Number of frames to play.
train_frames = TRAIN_FRAMES
filename = params_to_filename(params, mode, train_frames)
print(filename)
if mode == 'lane_following':
rate = 10 # Hz
screen = pygame.display.set_mode((1300, 600))
pygame.display.set_caption("mdeyo car sim")
background = pygame.Surface(screen.get_size())
background.fill((0, 0, 0))
RED = (255, 0, 0)
car = Car2(RED, 60, 385, screen, 100)
road = CurvedRoad(1200, 60, 385, '45')
car.constant_speed = True
state = road.getState(car)
print('state:', state)
if mode == 'grid':
# Create a new game instance.
# game_state = carmunk.GameState()
grid = Grid(X_DIM, Y_DIM)
car = Car(grid, 0, 0)
game_state = World(grid, car, 500, 10, False)
# Get initial state by doing nothing and getting the state.
_, state = game_state.updateState(0)
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_reward = -999999
car_reward = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S')
loss_log = []
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
if mode == 'grid':
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 3) # random
else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
#reward, new_state = game_state.frame_step(action)
car_reward, new_state = game_state.updateState(action)
# car_reward = reward
# print(reward)
elif mode == 'lane_following':
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 3) # random
# actions currently are 0 = no input (drive straight)
# 1 = left turn input
# 2 = right turn input
else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
# print(action)
car.takeAction(action)
car.update(1 / rate)
road.plotRoad(screen)
new_state = road.getState(car)
(car_reward, done) = road.reward(car)
# --- Go ahead and update the screen with what we've drawn.
pygame.display.flip()
# --- Limit to 60 frames per second
# clock.tick(rate)
# print(car_reward)
# Experience replay storage.
print(t, 'reward', car_reward)
# print('state:', state, 'action', action, 'reward',
# car_reward, 'new_state', new_state)
replay.append((state, action, car_reward, new_state))
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
epochs=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
# print(state)
# game_state.grid.printGrid()
# print(reward)
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1 / train_frames)
# We died, so update stuff.
if done == 1:
# if reward > 0 or reward==-999:
# Log the car's distance at this T.
data_collect.append([t, car_reward])
# Update max.
if car_reward > max_car_reward:
max_car_reward = car_reward
# Time it.
tot_time = timeit.default_timer() - start_time
# fps = car_distance / tot_time
# Output some stuff so we can watch.
print("Max: %d at %d\tepsilon %f\t(%d)\t" %
(max_car_reward, t, epsilon, car_reward))
# Reset.
car_reward = 0
start_time = timeit.default_timer()
# Save the model every 25,000 frames.
if t % 100 == 0:
print(t)
if t % 2000 == 0:
model.save_weights('saved-models/' + filename + '-' + str(t) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
# Log results after we're done all frames.
print(train_frames)
log_results(filename, data_collect, loss_log, train_frames, observe)
def train_net(model, params):
filename = params_to_filename(params)
observe = 129 # Number of frames to observe before training.
epsilon = 0.5
train_frames = 50000 # Number of frames to play.
steps = 0
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S'). #to be displayed
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
_, state, _ = game_state.frame_step((2))
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
print(t)
t += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 5) # random
else:
# Get Q values for each action.
print("PREDICTED", state)
# time.sleep(1)
x = state[0]
y = state[1]
qval = model.predict(np.array([x, y]).reshape((1, 2)),
batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
reward, new_state, term = game_state.frame_step(action)
print("timestep :" + str(t) + "Reward" + str(reward) + "action" +
str(action) + "state" + str(state))
# Experience replay storage.
replay.append((state, action, reward, new_state))
# print(len(replay))
# If we're done observing, start training.
if t > observe:
#print("start")
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch2(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
steps += 1
if steps % 1000 == 0:
print("Step = " + str(steps), "Epsilon = " + str(epsilon))
# Update the starting state with S'.
state = new_state
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (10.0 / train_frames)
print("EPSILON UPDATED", epsilon)
# We died, so update stuff.
if term == 1:
# print("Crashed.")
# Log the car's distance at this T.
data_collect.append([t, car_distance])
continue
# Reset.
car_distance = 0
# We reached the goal, so update stuff.
elif term == 2:
print("Reached goal.", car_distance)
# Log the car's distance at this T.
data_collect.append([t, car_distance])
continue
# Reset.
car_distance = 0
# Save the model every 25,000 frames.
if t % 25000 == 0:
model.save_weights('saved-models/' + filename + '-' + str(t) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
# if(keyboard.is_pressed('8')):
# print("Reset Goal")
# game_state.reset_goal()
print(t, reward, action)
def train_net(model, params):
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 1002 # Number of frames to play.
reward = 0
death = 0
printstuff = ''
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
max_reward = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = carmunkStatic.GameState()
# Get initial state by doing nothing and getting the state.
_, state, nothing = game_state.frame_step((2))
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 4) # random 0-1-2
else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
reward, new_state, printstuff = game_state.frame_step(action)
# Experience replay storage.
replay.append((state, action, reward, new_state))
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1 / train_frames)
#Update max
if reward > max_reward:
max_reward = reward
# We died, so update stuff.
if reward == -500:
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Update max.
if car_distance > max_car_distance:
max_car_distance = car_distance
# Time it.
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
# Output some stuff so we can watch.
print("Max: %d at %d\tepsilon %f\t(%d)\t%f fps" %
(max_car_distance, t, epsilon, car_distance, fps))
print("Max reward : %d", max_reward)
# Reset.
car_distance = 0
start_time = timeit.default_timer()
#update death
death += 1
if t > observe & death > 10:
return
print(printstuff)
def train_net(best_action_model, params):
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 500000 # Number of frames to play. was 1000000
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
t = 0
cum_rwd = 0
cum_rwd_read = 0
cum_rwd_dist = 0
cum_rwd_speed = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
save_init = True
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
state, new_reward, cur_speed, _, _, _ = game_state.frame_step(
START_ACTION, START_SPEED, START_DISTANCE)
# frame_step returns reward, state, speed
#state = state_frames(state, np.array([[0, 0, 0, 0, 0, 0, 0]])) # zeroing distance readings
#state = state_frames(state, np.zeros((1,NUM_SENSORS))) # zeroing distance readings
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
#time.sleep(0.5)
t += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, NUM_OUTPUT) # random
else:
# Get Q values for each action
qval = best_action_model.predict(state, batch_size=1)
# best_action_model was passed to this function. call it w/ current state
action = (np.argmax(qval)) # best prediction
# Take action, observe new state and get our treat.
new_state, new_reward, new_speed, new_rwd_read, new_rwd_dist, new_rwd_speed = \
game_state.frame_step(action, cur_speed, car_distance)
# Use multiple frames.
#new_state = state_frames(new_state, state) # seems this is appending 2-3 moves, results
# Experience replay storage.
replay.append((state, action, new_reward, new_state))
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# WHY RANDOM SAMPLE? COULD TRAINING BE SPED UP BY TAKING LAST BATCHSIZE
# Get training values.
X_train, y_train = process_minibatch(minibatch, best_action_model)
# Train the best_action_model on this batch.
history = LossHistory()
best_action_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
cur_speed = new_speed
cum_rwd += new_reward
cum_rwd_read += new_rwd_read
cum_rwd_dist += new_rwd_dist
cum_rwd_speed += new_rwd_speed
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1 / train_frames)
# We died, so update stuff.
if new_reward == -500 or new_reward == -1000:
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Update max.
if car_distance > max_car_distance:
max_car_distance = car_distance
# Time it.
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
# Output some stuff so we can watch.
print("Max: %d at %d\t eps: %f\t dist: %d\t rwd: %d\t read: %d\t dist: %d\t speed: %d\t fps: %d" %
(max_car_distance, t, epsilon, car_distance, cum_rwd, \
cum_rwd_read, cum_rwd_dist, cum_rwd_speed, int(fps)))
# Reset.
car_distance = 0
cum_rwd = 0
cum_rwd_read = 0
cum_rwd_dist = 0
cum_rwd_speed = 0
start_time = timeit.default_timer()
# Save early best_action_model, then every 20,000 frames
if t % 50000 == 0:
save_init = False
best_action_model.save_weights('saved-best_action_models/' +
filename + '-' + str(t) + '.h5',
overwrite=True)
print("Saving best_action_model %s - %d" % (filename, t))
# Log results after we're done all frames.
log_results(filename, data_collect, loss_log)
def train_net(model, params):
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 300000 # Number of frames to play.
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
#needed to print information
global max_reward
global stuff
global b_state
global max_qVal
frame = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
_, state, stuff = game_state.frame_step((2))
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
frame += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 4) # random 0-1-2-3
else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
reward, new_state, somestuff = game_state.frame_step(action)
if reward > max_reward:
stuff = somestuff
# Experience replay storage.
replay.append((state, action, reward, new_state))
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1 / train_frames)
# We died, so update stuff.
if reward == -500:
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Update max.
if car_distance > max_car_distance:
max_car_distance = car_distance
# Time it.
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
# Output some stuff so we can watch.
print("\n\nMax distance: %d at %d\nepsilon %f\n(%d)\n%f fps" %
(max_car_distance, t, epsilon, car_distance, fps))
print("\n Max reward : %d\t,\n max qVal : %d\t" %
(max_reward, max_qVal))
print('best state', b_state)
print(stuff)
print("\n frame:", frame)
# Reset.
max_reward = 0
stuff = ''
car_distance = 0
max_qVal = 0
b_state = [0, 0, 0, 0, 0, 0, 0, 0]
start_time = timeit.default_timer()
# Save the model every 25,000 frames.
if t % 25000 == 0:
model.save_weights('saved-models/BLE/final/' + 'FINAL' + filename +
'-' + str(t) + '.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
# Log results after we're done all frames.
log_results(filename, data_collect, loss_log)
def train_net(model, params):
filename = params_to_filename(params)
train_frames = 300000 # Number of frames to play.
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
t = 0
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = flappy.Game()
game_state.init_elements()
# Get initial state by doing nothing and getting the state.
state, _ = game_state.frame_step(0)
# Run the frames.
while t < train_frames:
t += 1
# Choose an action.
qval = model.predict(np.array([state]))[0]
action = (np.argmax(qval)) # best
if t % 500 == 0:
print(qval)
# Take action, observe new state and get our treat.
new_state, reward = game_state.frame_step(action)
if t % 1000 == 0:
print(t, action, state, reward)
# Experience replay storage.
replay.append((state, action, reward, new_state))
# If we're done observing, start training.
if t > batchSize:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
state = new_state
if reward == -1000:
game_state.init_elements()
state, _ = game_state.frame_step(0)
# Save the model every 2500 frames.
if t % 25000 == 0:
model.save_weights('results/saved-models/' + filename + '-' +
str(t) + '.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
if t % 50000 == 0:
# Log results after we're done all frames.
log_results(filename, loss_log)
def train(self,state,simulator):
self.t+=1
if random.random()<self.epsilon or self.t<self.observe:
action = np.random.randint(0, 4)
else:
# Get Q values for each action.
qval = self.model.predict(state, batch_size=1)
action = (np.argmax(qval))
# Take action, observe new state and get our treat.
simulator.applyAction(action)
reward, new_state = simulator.statusVector()
# Experience replay storage.
self.replay.append((state, action, reward, new_state))
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(self.replay) > buffer:
self.replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(self.replay, self.batchSize)
# Get training values.
X_train, y_train = process_minibatch2(minibatch, self.model)
# Train the model on this batch.
history = LossHistory()
model.fit(
X_train, y_train, batch_size=batchSize,
nb_epoch=1, verbose=0, callbacks=[history]
)
# Decrement epsilon over time.
if self.epsilon > 0.1 and self.t > self.observe:
self.epsilon -= (1.0/train_frames)
if self.t % 25000 == 0:
self.model.save_weights('saved-models/' + self._filename + '-' +
str(self.t) + '.h5',
overwrite=True)
print("Saving model %s - %d" % (self._filename, self.t))
'''TODO need to change to class functions'''
def process_minibatch2(minibatch, model):
# by Microos, improve this batch processing function
# and gain 50~60x faster speed (tested on GTX 1080)
# significantly increase the training FPS
# instead of feeding data to the model one by one,
# feed the whole batch is much more efficient
mb_len = len(minibatch)
old_states = np.zeros(shape=(mb_len, 5))
actions = np.zeros(shape=(mb_len,))
rewards = np.zeros(shape=(mb_len,))
new_states = np.zeros(shape=(mb_len, 5))
for i, m in enumerate(minibatch):
old_state_m, action_m, reward_m, new_state_m = m
old_states[i, :] = old_state_m[...]
actions[i] = action_m
rewards[i] = reward_m
new_states[i, :] = new_state_m[...]
old_qvals = model.predict(old_states, batch_size=mb_len)
new_qvals = model.predict(new_states, batch_size=mb_len)
maxQs = np.max(new_qvals, axis=1)
y = old_qvals
non_term_inds = np.where(rewards != -500)[0]
term_inds = np.where(rewards == -500)[0]
y[non_term_inds, actions[non_term_inds].astype(int)] = rewards[non_term_inds] + (GAMMA * maxQs[non_term_inds])
y[term_inds, actions[term_inds].astype(int)] = rewards[term_inds]
X_train = old_states
y_train = y
return X_train, y_train
def train_net(turn_model, turn_model_30, turn_model_50, turn_model_70,
avoid_model, acquire_model, acquire_model_30, acquire_model_50,
acquire_model_70, hunt_model, pack_model, params):
filename = params_to_filename(params)
if cur_mode in [TURN, HUNT, PACK]:
observe = 2000 # Number of frames to observe before training.
else:
observe = 2000
epsilon = 1 # vary this based on pre-learning already occurred in lower models
train_frames = 750000 # number of flips for training
batchSize = params['batchSize']
buffer = params['buffer']
# initialize variables and structures used below.
max_crash_frame_ctr = 0
crash_frame_ctr = 0
total_frame_ctr = 0
replay_frame_ctr = 0
stop_ctr = 0
avoid_ctr = 0
acquire_ctr = 0
cum_rwd = 0
cum_speed = 0
data_collect = []
replay = []
loss_log = [] # replay stores state, action, reward, new state
save_init = True
cur_speeds = []
for i in range(NUM_DRONES):
cur_speeds.append(START_SPEED)
# initialize drone state holders
turn_states = np.zeros(
[NUM_DRONES, TURN_TOTAL_SENSORS * TURN_STATE_FRAMES])
avoid_states = np.zeros(
[NUM_DRONES, AVOID_TOTAL_SENSORS * AVOID_STATE_FRAMES])
acquire_states = np.zeros(
[NUM_DRONES, ACQUIRE_NUM_SENSOR * ACQUIRE_STATE_FRAMES])
hunt_states = np.zeros(
[NUM_DRONES, HUNT_TOTAL_SENSORS * HUNT_STATE_FRAMES])
drone_states = np.zeros(
[NUM_DRONES, DRONE_TOTAL_SENSOR * PACK_STATE_FRAMES])
# create game instance
game_state = carmunk.GameState()
# get initial state(s)
turn_state, avoid_state, acquire_state, hunt_state, drone_state, reward, cur_speed = \
game_state.frame_step(START_DRONE_ID, START_TURN_ACTION, START_SPEED_ACTION,
START_PACK_ACTION, START_SPEED, START_DISTANCE, 1)
# initialize frame states
if cur_mode in [TURN, AVOID, HUNT, PACK]:
for i in range(NUM_DRONES):
turn_states[i] = state_frames(
turn_state,
np.zeros((1, TURN_TOTAL_SENSORS * TURN_STATE_FRAMES)),
TURN_TOTAL_SENSORS, TURN_STATE_FRAMES)
if cur_mode in [AVOID, HUNT, PACK]:
for i in range(NUM_DRONES):
avoid_states[i] = state_frames(
avoid_state,
np.zeros((1, AVOID_TOTAL_SENSORS * AVOID_STATE_FRAMES)),
AVOID_TOTAL_SENSORS, AVOID_STATE_FRAMES)
if cur_mode in [ACQUIRE, HUNT, PACK]:
for i in range(NUM_DRONES):
acquire_states[i] = state_frames(
acquire_state,
np.zeros((1, ACQUIRE_NUM_SENSOR * ACQUIRE_STATE_FRAMES)),
ACQUIRE_NUM_SENSOR, ACQUIRE_STATE_FRAMES)
if cur_mode in [HUNT, PACK]:
for i in range(NUM_DRONES):
hunt_states[i] = state_frames(
hunt_state,
np.zeros((1, HUNT_TOTAL_SENSORS * HUNT_STATE_FRAMES)),
HUNT_TOTAL_SENSORS, HUNT_STATE_FRAMES)
if cur_mode == PACK:
for i in range(NUM_DRONES):
drone_states[i] = state_frames(
drone_state,
np.zeros((1, DRONE_TOTAL_SENSOR * PACK_STATE_FRAMES)),
DRONE_TOTAL_SENSOR, PACK_STATE_FRAMES)
pack_state = state_frames(
drone_state, np.zeros((1, PACK_TOTAL_SENSORS * PACK_STATE_FRAMES)),
PACK_TOTAL_SENSORS, PACK_STATE_FRAMES)
# time it
start_time = timeit.default_timer()
# run frames
while total_frame_ctr < train_frames:
total_frame_ctr += 1 # counts total training distance traveled
crash_frame_ctr += 1 # counts distance between crashes
replay_frame_ctr += 1 # counts frames between pack mode replay captures
# used to slow things down for de-bugging
#time.sleep(0.25)
for drone_id in range(
NUM_DRONES): # NUM_DRONES = 1, unless you're in PACK mode
speed_action = START_SPEED_ACTION
# choose appropriate action(s)
# note: only generates random inputs for currently training model.
# all prior (sub) models provide their best (fully-trained) inputs
if random.random(
) < epsilon or total_frame_ctr < observe: # epsilon degrades over flips...
if cur_mode == TURN:
turn_action = set_turn_action(
True, cur_speeds[drone_id],
np.array([turn_states[drone_id]]))
else:
if cur_mode in [AVOID, HUNT, PACK]:
turn_action, turn_model = set_turn_action(
False, cur_speeds[drone_id],
np.array([turn_states[drone_id]]))
if cur_mode == AVOID:
speed_action = set_avoid_action(
True, turn_action,
np.array([avoid_states[drone_id]]))
else:
if cur_mode in [HUNT, PACK]:
speed_action = set_avoid_action(
False, turn_action,
np.array([avoid_states[drone_id]]))
if cur_mode == ACQUIRE:
acquire_action = set_acquire_action(
True, cur_speeds[drone_id],
np.array([acquire_states[drone_id, ]]))
turn_action = acquire_action
else:
acquire_action, acquire_model = set_acquire_action(
False, cur_speeds[drone_id],
np.array([acquire_states[drone_id, ]]))
if cur_mode == HUNT:
hunt_action, turn_action, speed_action = set_hunt_action(
True, cur_speeds[drone_id], turn_action,
speed_action, acquire_action,
np.array([hunt_states[drone_id, ]]))
else:
hunt_action, turn_action, speed_action = set_hunt_action(
False, cur_speeds[drone_id], turn_action,
speed_action, acquire_action,
np.array([hunt_states[drone_id, ]]))
if cur_mode == PACK and (
total_frame_ctr == 1 or
(replay_frame_ctr - 1) % PACK_EVAL_FRAMES
== 0) and drone_id == 0:
pack_action = set_pack_action(
True, pack_state)
# note: pack action only changed every PACK_EVAL_FRAMES.
# for frames in between it's constant
else: # ...increasing use of predictions over time
if cur_mode == TURN:
turn_action, turn_model = set_turn_action(
False, cur_speeds[drone_id],
np.array([turn_states[drone_id]]))
else:
if cur_mode in [AVOID, HUNT, PACK]:
turn_action, turn_model = set_turn_action(
False, cur_speeds[drone_id],
np.array([turn_states[drone_id]]))
if cur_mode == AVOID:
speed_action = set_avoid_action(
False, turn_action,
np.array([avoid_states[drone_id]]))
else:
if cur_mode in [HUNT, PACK]:
speed_action = set_avoid_action(
False, turn_action,
np.array([avoid_states[drone_id]]))
if cur_mode == ACQUIRE:
acquire_action, acquire_model = set_acquire_action(
False, cur_speeds[drone_id],
np.array([acquire_states[drone_id, ]]))
turn_action = acquire_action
else:
acquire_action, acquire_model = set_acquire_action(
False, cur_speeds[drone_id],
np.array([acquire_states[drone_id, ]]))
if cur_mode == HUNT:
hunt_action, turn_action, speed_action = set_hunt_action(
False, cur_speeds[drone_id], turn_action,
speed_action, acquire_action,
np.array([hunt_states[drone_id, ]]))
else:
hunt_action, turn_action, speed_action = set_hunt_action(
False, cur_speeds[drone_id], turn_action,
speed_action, acquire_action,
np.array([hunt_states[drone_id, ]]))
if cur_mode == PACK and (
total_frame_ctr == 1 or
(replay_frame_ctr - 1) % PACK_EVAL_FRAMES
== 0) and drone_id == 0:
# get 1 pack action for each set of drones on first drone
pack_action = set_pack_action(
False, pack_state)
print(pack_action)
#print("++++++ pack action:", pack_action)
#print(2)
# pass action, receive new state, reward
new_turn_state, new_avoid_state, new_acquire_state, new_hunt_state, new_drone_state, new_reward, new_speed = game_state.frame_step(
drone_id, turn_action, speed_action, pack_action,
cur_speeds[drone_id], total_frame_ctr, replay_frame_ctr)
#print("********** 2. new states / rewards:")
#print(total_frame_ctr)
#print(drone_id)
#print(new_drone_state)
#print(new_reward)
#print(3)
# append (horizontally) historical states for learning speed.
""" note: do this concatination even for models that are not learning (e.g., turn when running search or turn, search and acquire while running hunt) b/c their preds, performed above, expect the same multi-frame view that was in place when they trained."""
if cur_mode in [TURN, AVOID, HUNT, PACK]:
new_turn_state = state_frames(
new_turn_state, np.array([turn_states[drone_id]]),
TURN_TOTAL_SENSORS, TURN_STATE_FRAMES)
if cur_mode in [AVOID, HUNT, PACK]:
new_avoid_state = state_frames(
new_avoid_state, np.array([avoid_states[drone_id]]),
AVOID_TOTAL_SENSORS, AVOID_STATE_FRAMES)
if cur_mode in [ACQUIRE, HUNT, PACK]:
new_acquire_state = state_frames(
new_acquire_state, np.array([acquire_states[drone_id]]),
ACQUIRE_NUM_SENSOR, ACQUIRE_STATE_FRAMES)
if cur_mode in [HUNT, PACK]:
new_hunt_state = state_frames(
new_hunt_state, np.array([hunt_states[drone_id]]),
HUNT_TOTAL_SENSORS, HUNT_STATE_FRAMES)
#print(4)
if cur_mode == PACK and (total_frame_ctr == 1 or
replay_frame_ctr % PACK_EVAL_FRAMES == 0):
if drone_id == 0: # for 1st drone, pack state = drone state
new_pack_state = new_drone_state
pack_rwd = new_reward
else: # otherwise, append drone record to prior drone state
new_pack_state = state_frames(new_pack_state,
new_drone_state,
DRONE_TOTAL_SENSOR, 2)
pack_rwd += new_reward
new_drone_state = state_frames(
new_drone_state, np.array([drone_states[drone_id]]),
DRONE_TOTAL_SENSOR, PACK_STATE_FRAMES)
if drone_id == (NUM_DRONES -
1): # for last drone build pack record
if total_frame_ctr == 1:
pack_state = np.zeros(
(1, PACK_TOTAL_SENSORS * PACK_STATE_FRAMES))
new_pack_state = state_frames(
new_pack_state, pack_state, PACK_TOTAL_SENSORS,
PACK_STATE_FRAMES
) #may need to add 1 to PACK_STATE_FRAMES
#print("**** 3. final pack reward:")
#print(pack_rwd)
#print(5)
# experience replay storage
"""note: only the model being trained requires event storage as it is stack that will be sampled for training below."""
if cur_mode == TURN:
replay.append((np.array([turn_states[drone_id]]), turn_action,
new_reward, new_turn_state))
elif cur_mode == AVOID:
replay.append((np.array([avoid_states[drone_id]]),
speed_action, new_reward, new_avoid_state))
elif cur_mode == ACQUIRE:
replay.append((np.array([acquire_states[drone_id]]),
turn_action, new_reward, new_acquire_state))
elif cur_mode == HUNT:
replay.append((np.array([hunt_states[drone_id]]), hunt_action,
new_reward, new_hunt_state))
elif cur_mode == PACK and (total_frame_ctr == 1
or replay_frame_ctr % PACK_EVAL_FRAMES
== 0) and drone_id == (NUM_DRONES - 1):
replay.append(
(pack_state, pack_action, pack_rwd, new_pack_state))
#print(replay[-1])
#print("6a")
# If we're done observing, start training.
if total_frame_ctr > observe and (
cur_mode != PACK or
(replay_frame_ctr % PACK_EVAL_FRAMES == 0
and drone_id == (NUM_DRONES - 1))):
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
if cur_mode == TURN:
# Get training values.
X_train, y_train = process_minibatch(
minibatch, turn_model, TURN_NUM_INPUT, TURN_NUM_OUTPUT)
history = LossHistory()
turn_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
elif cur_mode == AVOID:
X_train, y_train = process_minibatch(
minibatch, avoid_model, AVOID_NUM_INPUT,
AVOID_NUM_OUTPUT)
history = LossHistory()
avoid_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
elif cur_mode == ACQUIRE:
X_train, y_train = process_minibatch(
minibatch, acquire_model, ACQUIRE_NUM_INPUT,
ACQUIRE_NUM_OUTPUT)
history = LossHistory()
acquire_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
elif cur_mode == HUNT:
X_train, y_train = process_minibatch(
minibatch, hunt_model, HUNT_NUM_INPUT, HUNT_NUM_OUTPUT)
history = LossHistory()
hunt_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
elif cur_mode == PACK:
X_train, y_train = process_minibatch(
minibatch, pack_model, PACK_NUM_INPUT, PACK_NUM_OUTPUT)
history = LossHistory()
pack_model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
if cur_mode in [TURN, AVOID, HUNT, PACK]:
turn_states[drone_id] = new_turn_state
if cur_mode in [AVOID, HUNT, PACK]:
avoid_states[drone_id] = new_avoid_state
if cur_mode in [ACQUIRE, HUNT, PACK]:
acquire_states[drone_id] = new_acquire_state
if cur_mode in [HUNT, PACK]:
hunt_states[drone_id] = new_hunt_state
if cur_mode == PACK and (total_frame_ctr == 1 or
replay_frame_ctr % PACK_EVAL_FRAMES == 0):
drone_states[drone_id] = new_drone_state
if drone_id == (NUM_DRONES - 1):
pack_state = new_pack_state
replay_frame_ctr = 0
cur_speeds[drone_id] = new_speed
cum_rwd += new_reward
# in case of crash, report and initialize
if new_reward == -500 or new_reward == -1000:
# Log the car's distance at this T.
data_collect.append([total_frame_ctr, crash_frame_ctr])
# Update max.
if crash_frame_ctr > max_crash_frame_ctr:
max_crash_frame_ctr = crash_frame_ctr
# Time it.
tot_time = timeit.default_timer() - start_time
fps = crash_frame_ctr / tot_time
# Output some stuff so we can watch.
#try:
print(
"Max: %d at %d\t eps: %f\t dist: %d\t mode: %d\t cum rwd: %d\t fps: %d"
% (max_crash_frame_ctr, total_frame_ctr, epsilon,
crash_frame_ctr, cur_mode, cum_rwd, int(fps)))
# break
#except (RuntimeError, TypeError, NameError):
# pass
# Reset.
crash_frame_ctr = cum_rwd = cum_speed = 0
start_time = timeit.default_timer()
#print(9)
# decrement epsilon for another frame
if epsilon > 0.1 and total_frame_ctr > observe:
epsilon -= (1 / train_frames)
if total_frame_ctr % 10000 == 0:
if crash_frame_ctr != 0:
#try:
print(
"Max: %d at %d\t eps: %f\t dist: %d\t mode: %d\t cum rwd: %d"
% (max_crash_frame_ctr, total_frame_ctr, epsilon,
crash_frame_ctr, cur_mode, cum_rwd))
# break
#except (RuntimeError, TypeError, NameError):
#pass
# Save model every 50k frames
if total_frame_ctr % 50000 == 0:
save_init = False
if cur_mode == TURN:
turn_model.save_weights('models/turn/turn-' + filename + '-' +
str(START_SPEED) + '-' +
str(total_frame_ctr) + '.h5',
overwrite=True)
print("Saving turn_model %s - %d - %d" %
(filename, START_SPEED, total_frame_ctr))
elif cur_mode == AVOID:
avoid_model.save_weights('models/avoid/avoid-' + filename +
'-' + str(total_frame_ctr) + '.h5',
overwrite=True)
print("Saving avoid_model %s - %d" %
(filename, total_frame_ctr))
elif cur_mode == ACQUIRE:
acquire_model.save_weights('models/acquire/acquire-' +
filename + '-' + str(START_SPEED) +
'-' + str(total_frame_ctr) + '.h5',
overwrite=True)
print("Saving acquire_model %s - %d" %
(filename, total_frame_ctr))
elif cur_mode == HUNT:
hunt_model.save_weights('models/hunt/hunt-' + filename + '-' +
str(total_frame_ctr) + '.h5',
overwrite=True)
print("Saving hunt_model %s - %d" %
(filename, total_frame_ctr))
elif cur_mode == PACK:
pack_model.save_weights('models/pack/pack-' + filename + '-' +
str(total_frame_ctr) + '.h5',
overwrite=True)
print("Saving pack_model %s - %d" %
(filename, total_frame_ctr))
# Log results after we're done all frames.
log_results(filename, data_collect, loss_log)
def train_net(model, params):
global counter
global lastState
global last_action
global lastreward
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.p
epsilon = 1
train_frames = 1000000 # Number of frames to play.
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
_, state = game_state.frame_step((2))
# state = np.array([14,14,14,14,14,14,14,14,14])
# state = np.expand_dims(state, axis = 0)
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 5) # random
else:
# Get Q values for each action.
qval = model.predict(train_new_state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
if lastreward < -100:
lastState = state
train_state = np.append(lastState, state[0])
train_state = np.append(train_state, last_action)
train_state = np.expand_dims(train_state, axis=0)
reward, new_state = game_state.frame_step(action)
train_new_state = np.append(state[0], new_state[0])
train_new_state = np.append(train_new_state, action)
train_new_state = np.expand_dims(train_new_state, axis=0)
if sum(state[0]) >= 42:
counter += 1
if counter % 40 == 0:
replay.append((train_state, action, reward, train_new_state))
if counter > 1000000000:
counter = 0
else:
replay.append((train_state, action, reward, train_new_state))
lastState = np.copy(state)
state = np.copy(new_state)
# Experience replay storage.
last_action = action
# If we're done observing, start training.
if t > observe:
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.batchSize
X_train, y_train = process_minibatch(minibatch, model)
# Train the model on this batch.
history = LossHistory()
batchSize1 = len(X_train)
model.fit(X_train,
y_train,
batch_size=batchSize1,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
# Update the starting state with S'.
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= 5 * (1 / train_frames)
# We died, so update stuff.
lastreward = reward
if reward == -500:
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Update max.
if car_distance > max_car_distance:
max_car_distance = car_distance
# Time it.
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
# Output some stuff so we can watch.
print("Max: %d at %d\tepsilon %f\t(%d)\t%f fps" %
(max_car_distance, t, epsilon, car_distance, fps))
# Reset.
car_distance = 0
start_time = timeit.default_timer()
# Save the model every 25,000 frames.
if t % 10000 == 0:
model.save_weights('saved-models/' + filename + '-' + str(t) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
# Log results after we're done all frames.
log_results(filename, data_collect, loss_log)
def train_net(model, params):
filename = params_to_filename(params)
observe = 1000
epsilon = 1
train_frames = 100000
batchSize = params['batchSize']
buffer = params['buffer']
max_car_distance = 0
car_distance = 0
t = 0
data_collect = []
replay = []
loss_log = []
game_state = UI.GameState()
_, state = game_state.frame_step((2))
start_time = timeit.default_timer()
while t < train_frames:
t += 1
car_distance += 1
if random.random() < epsilon or t < observe:
action = np.random.randint(0, 3)
else:
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval))
reward, new_state = game_state.frame_step(action)
replay.append((state, action, reward, new_state))
if t > observe:
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
X_train, y_train = process_minibatch2(minibatch, model)
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
state = new_state
if epsilon > 0.1 and t > observe:
epsilon -= (1.0 / train_frames)
if reward == -500:
data_collect.append([t, car_distance])
if car_distance > max_car_distance:
max_car_distance = car_distance
tot_time = timeit.default_timer() - start_time
fps = car_distance / tot_time
print("Max: %d at %d\tepsilon %f\t(%d)\t%f fps" %
(max_car_distance, t, epsilon, car_distance, fps))
car_distance = 0
start_time = timeit.default_timer()
# Save the model every 25,000 frames.
if t % 25000 == 0:
model.save_weights('saved-models/' + filename + '-' + str(t) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
log_results(filename, data_collect, loss_log)
def train_net(model, params):
filename = params_to_filename(params)
observe = 1000 # Number of frames to observe before training.
epsilon = 1
train_frames = 110000 # Number of frames to play.
steps = 0
batchSize = params['batchSize']
buffer = params['buffer']
# Just stuff used below.
max_car_distance = 0
car_distance = 0
t = 0
data_collect = []
replay = [] # stores tuples of (S, A, R, S').
loss_log = []
# Create a new game instance.
game_state = carmunk.GameState()
# Get initial state by doing nothing and getting the state.
_, state = game_state.frame_step((1))
# Let's time it.
start_time = timeit.default_timer()
# Run the frames.
while t < train_frames:
t += 1
car_distance += 1
# Choose an action.
if random.random() < epsilon or t < observe:
action = np.random.randint(3) # random
else:
# Get Q values for each action.
qval = model.predict(state, batch_size=1)
action = (np.argmax(qval)) # best
# Take action, observe new state and get our treat.
reward, new_state = game_state.frame_step(action)
# Experience replay storage.
replay.append((state, action, reward, new_state))
# If we're done observing, start training.
if t > observe:
#print("start")
# If we've stored enough in our buffer, pop the oldest.
if len(replay) > buffer:
replay.pop(0)
# Randomly sample our experience replay memory
minibatch = random.sample(replay, batchSize)
# Get training values.
X_train, y_train = process_minibatch2(minibatch, model)
# Train the model on this batch.
history = LossHistory()
model.fit(X_train,
y_train,
batch_size=batchSize,
nb_epoch=1,
verbose=0,
callbacks=[history])
loss_log.append(history.losses)
steps += 1
if steps % 1000 == 0:
print("Step = " + str(steps), "Epsilon = " + str(epsilon))
# Update the starting state with S'.
state = new_state
# Decrement epsilon over time.
if epsilon > 0.1 and t > observe:
epsilon -= (1.0 / train_frames)
# We died, so update stuff.
if reward <= -500:
#print("Crashed.")
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Reset.
car_distance = 0
# We reached the goal, so update stuff.
elif reward >= 2000:
print("Reached goal.")
# Log the car's distance at this T.
data_collect.append([t, car_distance])
# Reset.
car_distance = 0
# Save the model every 25,000 frames.
if t % 25000 == 0:
model.save_weights('saved-models/' + filename + '-' + str(t) +
'.h5',
overwrite=True)
print("Saving model %s - %d" % (filename, t))
