def __init__(self, actions, discount, featureExtractor, explorationProb,
stepSize, replay_memory_size, replay_memory_sample_size,
num_static_target_update_steps, maxGradient,
num_consecutive_random_actions):
"""
:note: please see parent class for params not described here
"""
super(QLearningReplayMemoryAlgorithm,
self).__init__(actions, discount, featureExtractor,
explorationProb, stepSize, maxGradient,
num_consecutive_random_actions)
self.replay_memory = ReplayMemory(replay_memory_size)
self.sample_size = replay_memory_sample_size
self.num_static_target_update_steps = num_static_target_update_steps
self.static_target_weights = copy.deepcopy(self.weights)
self.iterations = 0
def __init__(self, actions, discount, featureExtractor,
explorationProb, stepSize, replay_memory_size,
replay_memory_sample_size, num_static_target_update_steps,
maxGradient, num_consecutive_random_actions):
"""
:note: please see parent class for params not described here
"""
super(QLearningReplayMemoryAlgorithm, self).__init__(actions, discount, featureExtractor,
explorationProb, stepSize, maxGradient, num_consecutive_random_actions)
self.replay_memory = ReplayMemory(replay_memory_size)
self.sample_size = replay_memory_sample_size
self.num_static_target_update_steps = num_static_target_update_steps
self.static_target_weights = copy.deepcopy(self.weights)
self.iterations = 0
class QLearningReplayMemoryAlgorithm(ValueLearningAlgorithm):
"""
:description: Class implementing the Q-learning algorithm with
replay memory, which randomly samples from past experiences
in order to make updates to the model parameters.
"""
def __init__(self, actions, discount, featureExtractor, explorationProb,
stepSize, replay_memory_size, replay_memory_sample_size,
num_static_target_update_steps, maxGradient,
num_consecutive_random_actions):
"""
:note: please see parent class for params not described here
"""
super(QLearningReplayMemoryAlgorithm,
self).__init__(actions, discount, featureExtractor,
explorationProb, stepSize, maxGradient,
num_consecutive_random_actions)
self.replay_memory = ReplayMemory(replay_memory_size)
self.sample_size = replay_memory_sample_size
self.num_static_target_update_steps = num_static_target_update_steps
self.static_target_weights = copy.deepcopy(self.weights)
self.iterations = 0
def getStaticQ(self, state, action):
"""
:description: returns the Q value associated with this state-action pair, as
derived from the static target weights. This could be achieved through
the getQ function with a flag, but again trying to be clear about what we are
doing.
:type state: dictionary
:param state: the state of the game
:type action: int
:param action: the action for which to retrieve the Q-value
"""
score = 0
for f, v in self.featureExtractor(state, action):
score += self.static_target_weights[f] * v
return score
def update_static_target_function(self):
"""
:description: this updates the static target weights to the current weights.
This is a separate function so as to be clear what we are accomplishing
by setting the static_target_weights.
We use static target weights in order to prevent the algorithm from
diverging when a single, large update is made to the weights which leads
to a cycle of increasing weights.
"""
self.static_target_weights = copy.deepcopy(self.weights)
def incorporateFeedback(self, state, action, reward, newState):
"""
:description: performs a Q-learning update
:type state: dictionary
:param state: the state of the game
:type action: int
:param action: the action for which to retrieve the Q-value
:type reward: float
:param reward: reward associated with being in newState
:type newState: dictionary
:param newState: the new state of the game
:type rval: int or None
:param rval: if rval returned, then this is the next action taken
"""
# this check implements the static target by setting the weights to their current
# weights every self.num_static_target_update_steps
self.iterations += 1
if self.iterations % self.num_static_target_update_steps == 0:
self.update_static_target_function()
stepSize = self.stepSize
self.getStaticQ(newState, self.actions[0])
sars_tuple = (state, action, reward, newState)
self.replay_memory.store(sars_tuple)
num_samples = self.sample_size if self.replay_memory.isFull() else 1
for i in range(0, num_samples):
state, action, reward, newState = self.replay_memory.sample()
prediction = self.getQ(state, action)
target = reward
if newState != None:
# this is the line that implements the static target, but using the static weights
# for the target value
target += self.discount * max(
self.getStaticQ(newState, newAction)
for newAction in self.actions)
update = stepSize * (prediction - target)
update = np.clip(update, -self.maxGradient, self.maxGradient)
for f, v in self.featureExtractor(state, action):
self.weights[f] = self.weights[f] - update * v
assert (self.weights[f] < MAX_FEATURE_WEIGHT_VALUE)
# return None to denote that this is a off-policy algorithm
return None
class QLearningReplayMemoryAlgorithm(ValueLearningAlgorithm):
"""
:description: Class implementing the Q-learning algorithm with
replay memory, which randomly samples from past experiences
in order to make updates to the model parameters.
"""
def __init__(self, actions, discount, featureExtractor,
explorationProb, stepSize, replay_memory_size,
replay_memory_sample_size, num_static_target_update_steps,
maxGradient, num_consecutive_random_actions):
"""
:note: please see parent class for params not described here
"""
super(QLearningReplayMemoryAlgorithm, self).__init__(actions, discount, featureExtractor,
explorationProb, stepSize, maxGradient, num_consecutive_random_actions)
self.replay_memory = ReplayMemory(replay_memory_size)
self.sample_size = replay_memory_sample_size
self.num_static_target_update_steps = num_static_target_update_steps
self.static_target_weights = copy.deepcopy(self.weights)
self.iterations = 0
def getStaticQ(self, state, action):
"""
:description: returns the Q value associated with this state-action pair, as
derived from the static target weights. This could be achieved through
the getQ function with a flag, but again trying to be clear about what we are
doing.
:type state: dictionary
:param state: the state of the game
:type action: int
:param action: the action for which to retrieve the Q-value
"""
score = 0
for f, v in self.featureExtractor(state, action):
score += self.static_target_weights[f] * v
return score
def update_static_target_function(self):
"""
:description: this updates the static target weights to the current weights.
This is a separate function so as to be clear what we are accomplishing
by setting the static_target_weights.
We use static target weights in order to prevent the algorithm from
diverging when a single, large update is made to the weights which leads
to a cycle of increasing weights.
"""
self.static_target_weights = copy.deepcopy(self.weights)
def incorporateFeedback(self, state, action, reward, newState):
"""
:description: performs a Q-learning update
:type state: dictionary
:param state: the state of the game
:type action: int
:param action: the action for which to retrieve the Q-value
:type reward: float
:param reward: reward associated with being in newState
:type newState: dictionary
:param newState: the new state of the game
:type rval: int or None
:param rval: if rval returned, then this is the next action taken
"""
# this check implements the static target by setting the weights to their current
# weights every self.num_static_target_update_steps
self.iterations += 1
if self.iterations % self.num_static_target_update_steps == 0:
self.update_static_target_function()
stepSize = self.stepSize
self.getStaticQ(newState, self.actions[0])
sars_tuple = (state, action, reward, newState)
self.replay_memory.store(sars_tuple)
num_samples = self.sample_size if self.replay_memory.isFull() else 1
for i in range(0, num_samples):
state, action, reward, newState = self.replay_memory.sample()
prediction = self.getQ(state, action)
target = reward
if newState != None:
# this is the line that implements the static target, but using the static weights
# for the target value
target += self.discount * max(self.getStaticQ(newState, newAction) for newAction in self.actions)
update = stepSize * (prediction - target)
update = np.clip(update, -self.maxGradient, self.maxGradient)
for f, v in self.featureExtractor(state, action):
self.weights[f] = self.weights[f] - update * v
assert(self.weights[f] < MAX_FEATURE_WEIGHT_VALUE)
# return None to denote that this is a off-policy algorithm
return None
def train(gamepath, n_episodes, display_screen, record_weights, reduce_exploration_prob_amount, n_frames_to_skip, exploration_prob, verbose, discount, learning_rate, load_weights, frozen_target_update_period, use_replay_mem):
"""
:description: trains an agent to play a game
:type gamepath: string
:param gamepath: path to the binary of the game to be played
:type n_episodes: int
:param n_episodes: number of episodes of the game on which to train
display_screen : whether or not to display the screen of the game
record_weights : whether or not to save the weights of the nextwork
reduce_exploration_prob_amount : amount to reduce exploration prob each episode
to not reduce exploration_prob set to 0
n_frames_to_skip : how frequently to determine a new action to use
exploration_prob : probability of choosing a random action
verbose : whether or not to print information about the run periodically
discount : discount factor used in learning
learning_rate : the scaling factor for the sgd update
load_weights : whether or not to load weights for the network (set the files directly below)
frozen_target_update_period : the number of episodes between reseting the target of the network
"""
# load the ale interface to interact with
ale = ALEInterface()
ale.setInt('random_seed', 42)
# display/recording settings, doesn't seem to work currently
recordings_dir = './recordings/breakout/'
# previously "USE_SDL"
if display_screen:
if sys.platform == 'darwin':
import pygame
pygame.init()
ale.setBool('sound', False) # Sound doesn't work on OSX
#ale.setString("record_screen_dir", recordings_dir);
elif sys.platform.startswith('linux'):
ale.setBool('sound', True)
ale.setBool('display_screen', True)
ale.loadROM(gamepath)
ale.setInt("frame_skip", n_frames_to_skip)
# real actions for breakout are [0,1,3,4]
real_actions = ale.getMinimalActionSet()
# use a list of actions [0,1,2,3] to index into the array of real actions
actions = np.arange(len(real_actions))
# these theano variables are used to define the symbolic input of the network
features = T.dvector('features')
action = T.lscalar('action')
reward = T.dscalar('reward')
next_features = T.dvector('next_features')
# load weights by file name
# currently must be loaded by individual hidden layers
if load_weights:
hidden_layer_1 = file_utils.load_model('weights/hidden0_replay.pkl')
hidden_layer_2 = file_utils.load_model('weights/hidden1_replay.pkl')
else:
# defining the hidden layer network structure
# the n_hid of a prior layer must equal the n_vis of a subsequent layer
# for q-learning the output layer must be of len(actions)
hidden_layer_1 = HiddenLayer(n_vis=NNET_INPUT_DIMENSION,
n_hid=NNET_INPUT_DIMENSION, layer_name='hidden1', activation='relu')
hidden_layer_2 = HiddenLayer(n_vis=NNET_INPUT_DIMENSION,
n_hid=NNET_INPUT_DIMENSION, layer_name='hidden2', activation='relu')
hidden_layer_3 = HiddenLayer(n_vis=NNET_INPUT_DIMENSION,
n_hid=len(actions), layer_name='hidden3', activation='relu')
# the output layer is currently necessary when using tanh units in the
# hidden layer in order to prevent a theano warning
# currently the relu unit setting of the hidden and output layers is leaky w/ alpha=0.01
output_layer = OutputLayer(layer_name='output', activation='relu')
# pass a list of layers to the constructor of the network (here called "mlp")
layers = [hidden_layer_1, hidden_layer_2, hidden_layer_3, output_layer]
qnetwork = QNetwork(layers, discount=discount, learning_rate=learning_rate)
# this call gets the symbolic output of the network
# along with the parameter updates expected
loss, updates = qnetwork.get_loss_and_updates(features, action, reward, next_features)
# this defines the theano symbolic function used to train the network
# 1st argument is a list of inputs, here the symbolic variables above
# 2nd argument is the symbolic output expected
# 3rd argument is the dictionary of parameter updates
# 4th argument is the compilation mode
train_model = theano.function(
[theano.Param(features, default=np.zeros(NNET_INPUT_DIMENSION)),
theano.Param(action, default=0),
theano.Param(reward, default=0),
theano.Param(next_features, default=np.zeros(NNET_INPUT_DIMENSION))],
outputs=loss,
updates=updates,
mode='FAST_RUN')
sym_action = qnetwork.get_action(features)
get_action = theano.function([features], sym_action)
# some containers for collecting information about the training processes
rewards = []
losses = []
best_reward = 4
sequence_examples = []
sampled_examples = []
# the preprocessor and feature extractor to use
preprocessor = screen_utils.RGBScreenPreprocessor()
feature_extractor = feature_extractors.NNetOpenCVBoundingBoxExtractor(max_features=MAX_FEATURES)
if use_replay_mem:
replay_mem = ReplayMemory()
# main training loop, each episode is a full playthrough of the game
for episode in xrange(n_episodes):
# this implements the frozen target component of the network
# by setting the frozen layers of the network to a copy of the current layers
if episode % frozen_target_update_period == 0:
qnetwork.frozen_layers = copy.deepcopy(qnetwork.layers)
# some variables for collecting information about this particular run of the game
total_reward = 0
action = 1
counter = 0
reward = 0
loss = 0
previous_param_0 = None
# lives here is used for the reward heuristic of subtracting 1 from the reward
# when we lose a life. currently commented out this functionality because
# i think it might not be helpful.
lives = ale.lives()
# the initial state of the screen and state
screen = np.zeros((preprocessor.dim, preprocessor.dim, preprocessor.channels))
state = { "screen" : screen, "objects" : None, "prev_objects": None, "features": np.zeros(MAX_FEATURES)}
# start the actual play through of the game
while not ale.game_over():
counter += 1
# get the current features, which is the representation of the state provided to
# the "agent" (here just the network directly)
features = state["features"]
# epsilon greedy action selection (note that exploration_prob is reduced by
# reduce_exploration_prob_amount after every game)
if random.random() < exploration_prob:
action = random.choice(actions)
else:
# to choose an action from the network, we fprop
# the current state and take the argmax of the output
# layer (i.e., the action that corresponds to the
# maximum q value)
action = get_action(features)
# take the action and receive the reward
reward += ale.act(real_actions[action])
# this is commented out because i think it might not be helpful
if ale.lives() < lives:
lives = ale.lives()
reward -= 1
# get the next screen, preprocess it, initialize the next state
next_screen = ale.getScreenRGB()
next_screen = preprocessor.preprocess(next_screen)
next_state = {"screen": next_screen, "objects": None, "prev_objects": state["objects"]}
# get the features for the next state
next_features = feature_extractor(next_state, action=None)
if use_replay_mem:
sars_tuple = (features, action, reward, next_features)
replay_mem.store(sars_tuple)
num_samples = 5 if replay_mem.isFull() else 1
for i in range(0, num_samples):
random_train_tuple = replay_mem.sample()
loss += train_model(*random_train_tuple)
# collect for pca
sequence_examples.append(list(sars_tuple[0]) + [sars_tuple[1]] \
+ [sars_tuple[2]] + sars_tuple[3])
sequence_examples = sequence_examples[-100:]
sampled_examples.append(list(random_train_tuple[0]) + [random_train_tuple[1]] \
+ [random_train_tuple[2]] + random_train_tuple[3])
sampled_examples = sampled_examples[-100:]
else:
# call the train model function
loss += train_model(features, action, reward, next_features)
# prepare for the next loop through the game
next_state["features"] = next_features
state = next_state
# weird counter value to avoid interaction with any other counter
# loop that might be added, not necessary right now
if verbose and counter % PRINT_TRAINING_INFO_PERIOD == 0:
print('*' * 15 + ' training information ' + '*' * 15)
print('episode: {}'.format(episode))
print('reward: \t{}'.format(reward))
print('avg reward: \t{}'.format(np.mean(rewards)))
print 'avg reward (last 25): \t{}'.format(np.mean(rewards[-NUM_EPISODES_AVERAGE_REWARD_OVER:]))
print('action: \t{}'.format(real_actions[action]))
print('exploration prob: {}'.format(exploration_prob))
param_info = [(p.eval(), p.name) for p in qnetwork.get_params()]
for index, (val, name) in enumerate(param_info):
if previous_param_0 is None and index == 0:
previous_param_0 = val
print('parameter {} value: \n{}'.format(name, val))
if index == 0:
diff = val - previous_param_0
print('difference from previous param {}: \n{}'.format(name, diff))
print('features: \t{}'.format(features))
print('next_features: \t{}'.format(next_features))
scaled_sequence = preprocessing.scale(np.array(sequence_examples))
scaled_sampled = preprocessing.scale(np.array(sampled_examples))
pca = PCA()
_ = pca.fit_transform(scaled_sequence)
print('variance explained by first component for sequence: {}%'.format(pca. \
explained_variance_ratio_[0] * 100))
_ = pca.fit_transform(scaled_sampled)
print('variance explained by first component for sampled: {}%'.format(pca. \
explained_variance_ratio_[0] * 100))
print('*' * 52)
print('\n')
# collect info and total reward and also reset the reward to 0 if we reach this point
total_reward += reward
reward = 0
# collect stats from this game run
losses.append(loss)
rewards.append(total_reward)
# if we got a best reward, inform the user
if total_reward > best_reward:
best_reward = total_reward
print("best reward!: {}".format(total_reward))
# record the weights if record_weights=True
# must record the weights of the indiviual layers
# only save hidden layers b/c output layer does not have weights
if episode != 0 and episode % RECORD_WEIGHTS_PERIOD == 0 and record_weights:
file_utils.save_rewards(rewards)
file_utils.save_model(qnetwork.layers[0], 'weights/hidden0_{}.pkl'.format(episode))
file_utils.save_model(qnetwork.layers[1], 'weights/hidden1_{}.pkl'.format(episode))
# reduce exploration policy over time
if exploration_prob > MINIMUM_EXPLORATION_EPSILON:
exploration_prob -= reduce_exploration_prob_amount
# inform user of how the episode went and reset the game
print('episode: {} ended with score: {}\tloss: {}'.format(episode, rewards[-1], losses[-1]))
ale.reset_game()
# return the list of rewards attained
return rewards
