def __init__(self, settings):
assert(type(settings)==dict)
# seed
self.rng = settings['RNG']
# Epsilon
self.epsilon_start = settings['EPSILON_START']
self.epsilon_end = settings['EPSILON_END']
self.epsilon_end_time = settings['EPSILON_END_TIME']
self.testing_epsilon = settings['TESTING_EPSILON']
self.epsilon_decay = (self.epsilon_start-self.epsilon_end)/float(self.epsilon_end_time)
# Training
self.learning_rate = settings['LEARNING_RATE']
self.rmsprop_rho = settings['RMSPROP_RHO']
self.rmsprop_epsilon = settings['RMSPROP_EPSILON']
self.target_net_update = settings['TARGET_NET_UPDATE']
self.min_reward = settings['MIN_REWARD']
self.max_reward = settings['MAX_REWARD']
# Q-Learning Parameters
self.n_actions = settings['N_ACTIONS']
self.discount_factor = settings['DISCOUNT_FACTOR']
self.update_frequency = settings['UPDATE_FREQUENCY']
self.learn_start = settings['LEARN_START']
self.agent_history_length = settings['AGENT_HISTORY_LENGTH']
self.batch_size = settings['BATCH_SIZE']
# Preprocess
self.resize_width = settings['RESIZE_WIDTH']
self.resize_height = settings['RESIZE_HEIGHT']
self.resize_dims = (self.resize_width, self.resize_height)
self.net = DeepQNetwork(
self.n_actions,
self.agent_history_length,
self.resize_height,
self.resize_width
)
self.target_net = DeepQNetwork(
self.n_actions,
self.agent_history_length,
self.resize_height,
self.resize_width
)
self.target_net.setWeights(self.net.getWeights())
self.memory = ReplayMemory(settings)
self.numSteps = 0
self.lastState = None
self.lastAction = None
self.lastTerminal = None
self.compile()
class NeuralQLearner:
def __init__(self, settings):
assert(type(settings)==dict)
# seed
self.rng = settings['RNG']
# Epsilon
self.epsilon_start = settings['EPSILON_START']
self.epsilon_end = settings['EPSILON_END']
self.epsilon_end_time = settings['EPSILON_END_TIME']
self.testing_epsilon = settings['TESTING_EPSILON']
self.epsilon_decay = (self.epsilon_start-self.epsilon_end)/float(self.epsilon_end_time)
# Training
self.learning_rate = settings['LEARNING_RATE']
self.rmsprop_rho = settings['RMSPROP_RHO']
self.rmsprop_epsilon = settings['RMSPROP_EPSILON']
self.target_net_update = settings['TARGET_NET_UPDATE']
self.min_reward = settings['MIN_REWARD']
self.max_reward = settings['MAX_REWARD']
# Q-Learning Parameters
self.n_actions = settings['N_ACTIONS']
self.discount_factor = settings['DISCOUNT_FACTOR']
self.update_frequency = settings['UPDATE_FREQUENCY']
self.learn_start = settings['LEARN_START']
self.agent_history_length = settings['AGENT_HISTORY_LENGTH']
self.batch_size = settings['BATCH_SIZE']
# Preprocess
self.resize_width = settings['RESIZE_WIDTH']
self.resize_height = settings['RESIZE_HEIGHT']
self.resize_dims = (self.resize_width, self.resize_height)
self.net = DeepQNetwork(
self.n_actions,
self.agent_history_length,
self.resize_height,
self.resize_width
)
self.target_net = DeepQNetwork(
self.n_actions,
self.agent_history_length,
self.resize_height,
self.resize_width
)
self.target_net.setWeights(self.net.getWeights())
self.memory = ReplayMemory(settings)
self.numSteps = 0
self.lastState = None
self.lastAction = None
self.lastTerminal = None
self.compile()
def compile(self):
input_shape = (
self.batch_size,
self.agent_history_length,
self.resize_height,
self.resize_width
)
pred_input_shape = (
1,
self.agent_history_length,
self.resize_height,
self.resize_width
)
self.pred_input = shared(np.zeros(pred_input_shape, dtype=floatX))
self.net_input = shared(np.zeros(input_shape, dtype=floatX))
self.target_net_input = shared(np.zeros(input_shape, dtype=floatX))
self.shared_actions = shared(np.zeros((self.batch_size,), dtype='int32'))
self.shared_rewards = shared(np.zeros((self.batch_size,), dtype='int32'))
self.shared_terminals = shared(np.zeros((self.batch_size,), dtype=floatX))
actions = T.ivector()
rewards = T.ivector()
terminals = T.vector()
targets = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount_factor * T.max(self.target_net.qvalues, axis=1))
diff = targets - self.net.qvalues[T.arange(self.batch_size), actions]
qp = T.minimum(abs(diff), 1.0)
lp = abs(diff) - qp
delta = 0.5 * qp ** 2 + lp
cost = T.sum(delta)
optimizer = RMSprop(
cost,
self.net.params,
lr=self.learning_rate,
rho=self.rmsprop_rho,
epsilon=self.rmsprop_epsilon
)
givens = {
self.net.input: self.net_input,
self.target_net.input: self.target_net_input,
actions: self.shared_actions,
rewards: self.shared_rewards,
terminals: self.shared_terminals
}
self.train = function(
inputs=[],
outputs=cost,
updates=optimizer.getUpdates(),
givens=givens
)
self.prediction = function(
inputs=[],
outputs=self.net.qvalues.flatten(1),
givens={
self.net.input: self.pred_input
}
)
def preprocess(self, rawstate):
return cv2.resize(rawstate, self.resize_dims, interpolation=cv2.INTER_LINEAR)
def getEpsilon(self):
current_epsilon = self.epsilon_start - (self.numSteps * self.epsilon_decay)
return max(self.epsilon_end, current_epsilon)
def qLearnMinibatch(self):
s1, a, r, t, s2 = self.memory.sampleMinibatch()
# borrow=True para que no se haga una copia del arreglo y sea mas rapido
self.net_input.set_value(s1, borrow=True)
self.shared_actions.set_value(a, borrow=True)
self.shared_rewards.set_value(r, borrow=True)
self.shared_terminals.set_value(t, borrow=True)
self.target_net_input.set_value(s2, borrow=True)
return self.train()
def perceive(self, rawstate, reward, terminal, testing):
state = self.preprocess(rawstate)
reward = max(reward, self.min_reward)
reward = min(reward, self.max_reward)
self.memory.storeRecentState(state, terminal)
if((not testing) and (self.lastState is not None)):
self.memory.storeTransition(self.lastState, self.lastAction, reward, self.lastTerminal)
actionIndex = 0
if(not terminal):
actionIndex = self.eGreedy(testing)
flag1 = (self.numSteps > self.learn_start)
flag2 = (self.numSteps % self.update_frequency == 0)
# Short-Circuit-Eval ...
if((not testing) and flag1 and flag2):
cost = self.qLearnMinibatch()
if(self.numSteps % self.target_net_update == 0):
self.target_net.setWeights(self.net.getWeights())
self.lastState = state
self.lastAction = actionIndex
self.lastTerminal = terminal
if(not testing):
self.numSteps += 1
return actionIndex
def eGreedy(self, testing):
epsilon = self.testing_epsilon if(testing) else self.getEpsilon()
if(self.rng.uniform(0,1) < epsilon):
return self.rng.randint(0, self.n_actions)
else:
return self.greedy()
def greedy(self):
curState = self.memory.getRecentState()
curState = curState.reshape(1, curState.shape[0], curState.shape[1], curState.shape[2])
self.pred_input.set_value(curState, borrow=True)
q = self.prediction()
maxq = q[0]
besta = [0]
for a in xrange(1, self.n_actions):
if(q[a] > maxq):
maxq = q[a]
besta = [a]
elif(q[a] == maxq):
besta.append(a)
r = self.rng.randint(0, len(besta))
return besta[r]
