class Main:
# How many transitions to keep in memory?
memory_size = 500000
# Size of the mini-batch, 32 was given in the paper
minibatch_size = 32
# Number of possible actions in a given game, 6 for "Breakout"
number_of_actions = 6
# Size of one frame
frame_size = 84*84
# Size of one state is four 84x84 screens
state_size = 4 * frame_size
# Discount factor for future rewards
discount_factor = 0.9
# Exploration rate annealing speed
epsilon_frames = 1000000.0
# Epsilon during testing
test_epsilon = 0.05
# Total frames played, only incremented during training
total_frames_trained = 0
# Number of random states to use for calculating Q-values
nr_random_states = 100
# Random states that we use to calculate Q-values
random_states = None
# Memory itself
memory = None
# Neural net
nnet = None
# Communication with ALE
ale = None
def __init__(self):
self.memory = MemoryD(self.memory_size)
self.ale = ALE(self.memory)
self.nnet = NeuralNet(self.state_size, self.number_of_actions, "ai/deepmind-layers.cfg", "ai/deepmind-params.cfg", "layer4")
def compute_epsilon(self, frames_played):
"""
From the paper: "The behavior policy during training was epsilon-greedy
with annealed linearly from 1 to 0.1 over the first million frames, and fixed at 0.1 thereafter."
@param frames_played: How far are we with our learning?
"""
return max(1.0 - frames_played / self.epsilon_frames, 0.1)
def predict_best_action(self, last_state):
# last_state contains only one state, so we have to convert it into batch of size 1
last_state.shape = (last_state.shape[0], 1)
# use neural net to predict Q-values for all actions
qvalues = self.nnet.predict(last_state)
#print "Predicted action Q-values: ", qvalues
# return action (index) with maximum Q-value
return np.argmax(qvalues)
def train_minibatch(self, minibatch):
"""
Train function that transforms (state,action,reward,state) into (input, expected_output) for neural net
and trains the network
@param minibatch: list of arrays: prestates, actions, rewards, poststates
"""
prestates, actions, rewards, poststates = minibatch
# predict Q-values for prestates, so we can keep Q-values for other actions unchanged
qvalues = self.nnet.predict(prestates)
#print "Prestate q-values: ", qvalues[0,:]
#print "Action was: %d, reward was %d" % (actions[0], rewards[0])
# predict Q-values for poststates
post_qvalues = self.nnet.predict(poststates)
#print "Poststate q-values: ", post_qvalues[0,:]
# take maximum Q-value of all actions
max_qvalues = np.max(post_qvalues, axis = 1)
# update the Q-values for the actions we actually performed
for i, action in enumerate(actions):
qvalues[i][action] = rewards[i] + self.discount_factor * max_qvalues[i]
#print "Corrected q-values: ", qvalues[0,:]
# we have to transpose prediction result, as train expects input in opposite order
cost = self.nnet.train(prestates, qvalues.transpose().copy())
#qvalues = self.nnet.predict(prestates)
#print "After training: ", qvalues[0,:]
return cost
def play_games(self, nr_frames, train, epsilon = None):
"""
Main cycle: starts a game and plays number of frames.
@param nr_frames: total number of games allowed to play
@param train: true or false, whether to do training or not
@param epsilon: fixed epsilon, only used when not training
"""
assert train or epsilon is not None
frames_played = 0
game_scores = []
# Start a new game
self.ale.new_game()
game_score = 0
# Play games until maximum number is reached
while frames_played < nr_frames:
# Epsilon decreases over time only when training
if train:
epsilon = self.compute_epsilon(self.total_frames_trained)
#print "Current annealed epsilon is %f at %d frames" % (epsilon, self.total_frames_trained)
# Some times random action is chosen
if random.uniform(0, 1) < epsilon:
action = random.choice(range(self.number_of_actions))
#print "Chose random action %d" % action
# Usually neural net chooses the best action
else:
action = self.predict_best_action(self.memory.get_last_state())
#print "Neural net chose action %d" % int(action)
# Make the move
points = self.ale.move(action)
if points > 0:
print " Got %d points" % points
game_score += points
frames_played += 1
#print "Played frame %d" % frames_played
# Only if training
if train:
# Store new information to memory
self.ale.store_step(action)
# Increase total frames only when training
self.total_frames_trained += 1
# Fetch random minibatch from memory
minibatch = self.memory.get_minibatch(self.minibatch_size)
# Train neural net with the minibatch
self.train_minibatch(minibatch)
#print "Trained minibatch of size %d" % self.minibatch_size
# Play until game is over
if self.ale.game_over:
print " Game over, score = %d" % game_score
# After "game over" increase the number of games played
game_scores.append(game_score);
game_score = 0
# And do stuff after end game
self.ale.end_game()
self.ale.new_game()
# reset the game just in case
self.ale.end_game()
return game_scores
def run(self, epochs, training_frames, testing_frames):
# Open log files and write headers
timestamp = time.strftime("%Y-%m-%d-%H-%M")
log_train = open("../log/training_" + timestamp + ".csv", "w")
log_train.write("epoch,nr_games,sum_score,average_score,nr_frames,total_frames_trained,epsilon,memory_size\n")
log_test = open("../log/testing_" + timestamp + ".csv", "w")
log_test.write("epoch,nr_games,sum_score,average_score,average_qvalue,nr_frames,epsilon,memory_size\n")
log_train_scores = open("../log/training_scores_" + timestamp + ".txt", "w")
log_test_scores = open("../log/testing_scores_" + timestamp + ".txt", "w")
log_weights = open("../log/weights_" + timestamp + ".csv", "w")
for epoch in range(1, epochs + 1):
print "Epoch %d:" % epoch
if training_frames > 0:
# play number of frames with training and epsilon annealing
print " Training for %d frames" % training_frames
training_scores = self.play_games(training_frames, train = True)
# log training scores
log_train_scores.write(NL.join(map(str, training_scores)) + NL)
log_train_scores.flush()
# log aggregated training data
train_data = (epoch, len(training_scores), sum(training_scores), np.mean(training_scores), training_frames, self.total_frames_trained, self.compute_epsilon(self.total_frames_trained), self.memory.count)
log_train.write(','.join(map(str, train_data)) + NL)
log_train.flush()
weights = self.nnet.get_weight_stats()
if epoch == 1:
# write header
wlayers = []
for (layer, index) in weights:
wlayers.extend([layer, index, ''])
log_weights.write(','.join(wlayers) + NL)
wlabels = []
for (layer, index) in weights:
wlabels.extend(['weights', 'weightsInc', 'incRatio'])
log_weights.write(','.join(wlabels) + NL)
wdata = []
for w in weights.itervalues():
wdata.extend([str(w[0]), str(w[1]), str(w[1] / w[0] if w[0] > 0 else 0)])
log_weights.write(','.join(wdata) + NL)
log_weights.flush()
# save network state
self.nnet.save_network(epoch)
print # save_network()'s output doesn't include newline
if testing_frames > 0:
# play number of frames without training and without epsilon annealing
print " Testing for %d frames" % testing_frames
testing_scores = self.play_games(testing_frames, train = False, epsilon = self.test_epsilon)
# log testing scores
log_test_scores.write(NL.join(map(str, testing_scores)) + NL)
log_test_scores.flush()
# Pick random states to calculate Q-values for
if self.random_states is None and self.memory.count > self.nr_random_states:
print " Picking %d random states for Q-values" % self.nr_random_states
self.random_states = self.memory.get_minibatch(self.nr_random_states)[0]
# Do not calculate Q-values when mamory is empty
if self.random_states is not None:
# calculate Q-values
qvalues = self.nnet.predict(self.random_states)
assert qvalues.shape[0] == self.nr_random_states
assert qvalues.shape[1] == self.number_of_actions
max_qvalues = np.max(qvalues, axis = 1)
assert max_qvalues.shape[0] == self.nr_random_states
assert len(max_qvalues.shape) == 1
avg_qvalue = np.mean(max_qvalues)
else:
avg_qvalue = 0
# log aggregated testing data
test_data = (epoch, len(testing_scores), sum(testing_scores), np.mean(testing_scores), avg_qvalue, testing_frames, self.test_epsilon, self.memory.count)
log_test.write(','.join(map(str, test_data)) + NL)
log_test.flush()
log_train.close()
log_test.close()
log_train_scores.close()
log_test_scores.close()
log_weights.close()
class Main:
# How many transitions to keep in memory?
memory_size = 500000
# Size of the mini-batch, 32 was given in the paper
minibatch_size = 32
# Number of possible actions in a given game, 6 for "Breakout"
number_of_actions = 6
# Size of one frame
frame_size = 84*84
# Size of one state is four 84x84 screens
state_size = 4 * frame_size
# Discount factor for future rewards
discount_factor = 0.9
# Exploration rate annealing speed
epsilon_frames = 1000000.0
# Epsilon during testing
test_epsilon = 0.05
# Total frames played, only incremented during training
total_frames_trained = 0
# Number of random states to use for calculating Q-values
nr_random_states = 100
# Random states that we use to calculate Q-values
random_states = None
# Memory itself
memory = None
# Neural net
nnet = None
# Communication with ALE
ale = None
def __init__(self):
self.memory = MemoryD(self.memory_size)
self.ale = ALE(self.memory, display_screen="true", skip_frames=4, game_ROM='../libraries/ale/roms/breakout.bin')
self.nnet = NeuralNet(self.state_size, self.number_of_actions, "ai/deepmind-layers.cfg", "ai/deepmind-params.cfg", "layer4")
def compute_epsilon(self, frames_played):
"""
From the paper: "The behavior policy during training was epsilon-greedy
with annealed linearly from 1 to 0.1 over the first million frames, and fixed at 0.1 thereafter."
@param frames_played: How far are we with our learning?
"""
return max(1.0 - frames_played / self.epsilon_frames, 0.1)
def predict_best_action(self, last_state):
# last_state contains only one state, so we have to convert it into batch of size 1
last_state.shape = (last_state.shape[0], 1)
# use neural net to predict Q-values for all actions
qvalues = self.nnet.predict(last_state)
#print "Predicted action Q-values: ", qvalues
# return action (index) with maximum Q-value
return np.argmax(qvalues)
def train_minibatch(self, minibatch):
"""
Train function that transforms (state,action,reward,state) into (input, expected_output) for neural net
and trains the network
@param minibatch: list of arrays: prestates, actions, rewards, poststates
"""
prestates, actions, rewards, poststates = minibatch
# predict Q-values for prestates, so we can keep Q-values for other actions unchanged
qvalues = self.nnet.predict(prestates)
#print "Prestate q-values: ", qvalues[0,:]
#print "Action was: %d, reward was %d" % (actions[0], rewards[0])
# predict Q-values for poststates
post_qvalues = self.nnet.predict(poststates)
#print "Poststate q-values: ", post_qvalues[0,:]
# take maximum Q-value of all actions
max_qvalues = np.max(post_qvalues, axis = 1)
# update the Q-values for the actions we actually performed
for i, action in enumerate(actions):
qvalues[i][action] = rewards[i] + self.discount_factor * max_qvalues[i]
#print "Corrected q-values: ", qvalues[0,:]
# we have to transpose prediction result, as train expects input in opposite order
cost = self.nnet.train(prestates, qvalues.transpose().copy())
#qvalues = self.nnet.predict(prestates)
#print "After training: ", qvalues[0,:]
return cost
def play_games(self, nr_frames, train, epsilon = None):
"""
Main cycle: starts a game and plays number of frames.
@param nr_frames: total number of games allowed to play
@param train: true or false, whether to do training or not
@param epsilon: fixed epsilon, only used when not training
"""
assert train or epsilon is not None
frames_played = 0
game_scores = []
# Start a new game
self.ale.new_game()
game_score = 0
# Play games until maximum number is reached
while frames_played < nr_frames:
# Epsilon decreases over time only when training
if train:
epsilon = self.compute_epsilon(self.total_frames_trained)
#print "Current annealed epsilon is %f at %d frames" % (epsilon, self.total_frames_trained)
# Some times random action is chosen
if random.uniform(0, 1) < epsilon:
action = random.choice(range(self.number_of_actions))
#print "Chose random action %d" % action
# Usually neural net chooses the best action
else:
action = self.predict_best_action(self.memory.get_last_state())
#print "Neural net chose action %d" % int(action)
# Make the move
points = self.ale.move(action)
if points > 0:
print " Got %d points" % points
game_score += points
frames_played += 1
#print "Played frame %d" % frames_played
# Only if training
if train:
# Store new information to memory
self.ale.store_step(action)
# Increase total frames only when training
self.total_frames_trained += 1
# Fetch random minibatch from memory
minibatch = self.memory.get_minibatch(self.minibatch_size)
# Train neural net with the minibatch
self.train_minibatch(minibatch)
#print "Trained minibatch of size %d" % self.minibatch_size
# Play until game is over
if self.ale.game_over:
print " Game over, score = %d" % game_score
# After "game over" increase the number of games played
game_scores.append(game_score);
game_score = 0
# And do stuff after end game
self.ale.end_game()
self.ale.new_game()
# reset the game just in case
self.ale.end_game()
return game_scores
def run(self, epochs, training_frames, testing_frames):
# Open log files and write headers
timestamp = time.strftime("%Y-%m-%d-%H-%M")
log_train = open("../log/training_" + timestamp + ".csv", "w")
log_train.write("epoch,nr_games,sum_score,average_score,nr_frames,total_frames_trained,epsilon,memory_size\n")
log_test = open("../log/testing_" + timestamp + ".csv", "w")
log_test.write("epoch,nr_games,sum_score,average_score,average_qvalue,nr_frames,epsilon,memory_size\n")
log_train_scores = open("../log/training_scores_" + timestamp + ".txt", "w")
log_test_scores = open("../log/testing_scores_" + timestamp + ".txt", "w")
log_weights = open("../log/weights_" + timestamp + ".csv", "w")
for epoch in range(1, epochs + 1):
print "Epoch %d:" % epoch
if training_frames > 0:
# play number of frames with training and epsilon annealing
print " Training for %d frames" % training_frames
training_scores = self.play_games(training_frames, train = True)
# log training scores
log_train_scores.write(NL.join(map(str, training_scores)) + NL)
log_train_scores.flush()
# log aggregated training data
train_data = (epoch, len(training_scores), sum(training_scores), np.mean(training_scores), training_frames, self.total_frames_trained, self.compute_epsilon(self.total_frames_trained), self.memory.count)
log_train.write(','.join(map(str, train_data)) + NL)
log_train.flush()
weights = self.nnet.get_weight_stats()
if epoch == 1:
# write header
wlayers = []
for (layer, index) in weights:
wlayers.extend([layer, index, ''])
log_weights.write(','.join(wlayers) + NL)
wlabels = []
for (layer, index) in weights:
wlabels.extend(['weights', 'weightsInc', 'incRatio'])
log_weights.write(','.join(wlabels) + NL)
wdata = []
for w in weights.itervalues():
wdata.extend([str(w[0]), str(w[1]), str(w[1] / w[0] if w[0] > 0 else 0)])
log_weights.write(','.join(wdata) + NL)
log_weights.flush()
# save network state
self.nnet.save_network(epoch)
print # save_network()'s output doesn't include newline
if testing_frames > 0:
# play number of frames without training and without epsilon annealing
print " Testing for %d frames" % testing_frames
testing_scores = self.play_games(testing_frames, train = False, epsilon = self.test_epsilon)
# log testing scores
log_test_scores.write(NL.join(map(str, testing_scores)) + NL)
log_test_scores.flush()
# Pick random states to calculate Q-values for
if self.random_states is None and self.memory.count > self.nr_random_states:
print " Picking %d random states for Q-values" % self.nr_random_states
self.random_states = self.memory.get_minibatch(self.nr_random_states)[0]
# Do not calculate Q-values when mamory is empty
if self.random_states is not None:
# calculate Q-values
qvalues = self.nnet.predict(self.random_states)
assert qvalues.shape[0] == self.nr_random_states
assert qvalues.shape[1] == self.number_of_actions
max_qvalues = np.max(qvalues, axis = 1)
assert max_qvalues.shape[0] == self.nr_random_states
assert len(max_qvalues.shape) == 1
avg_qvalue = np.mean(max_qvalues)
else:
avg_qvalue = 0
# log aggregated testing data
test_data = (epoch, len(testing_scores), sum(testing_scores), np.mean(testing_scores), avg_qvalue, testing_frames, self.test_epsilon, self.memory.count)
log_test.write(','.join(map(str, test_data)) + NL)
log_test.flush()
log_train.close()
log_test.close()
log_train_scores.close()
log_test_scores.close()
log_weights.close()
