# OpenGym CartPole-v0
# -------------------
#
# This code demonstrates use a full DQN implementation
# to solve OpenGym CartPole-v0 problem.
#
# Made as part of blog series Let's make a DQN, available at:
# https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/
#
# author: Jaromir Janisch, 2016
import random, numpy, math, gym, sys
from keras import backend as K
import tensorflow as tf
#----------
HUBER_LOSS_DELTA = 1.0
LEARNING_RATE = 0.00025
#----------
def huber_loss(y_true, y_pred):
err = y_true - y_pred
cond = K.abs(err) < HUBER_LOSS_DELTA
L2 = 0.5 * K.square(err)
L1 = HUBER_LOSS_DELTA * (K.abs(err) - 0.5 * HUBER_LOSS_DELTA)
loss = tf.where(cond, L2, L1) # Keras does not cover where function in tensorflow :-(
return K.mean(loss)
#-------------------- BRAIN ---------------------------
from keras.models import Sequential
from keras.layers import *
from keras.optimizers import *
class Brain:
def __init__(self, stateCnt, actionCnt):
self.stateCnt = stateCnt
self.actionCnt = actionCnt
self.model = self._createModel()
self.model_ = self._createModel()
def _createModel(self):
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=stateCnt))
model.add(Dense(units=actionCnt, activation='linear'))
opt = RMSprop(lr=LEARNING_RATE)
model.compile(loss=huber_loss, optimizer=opt)
return model
def train(self, x, y, epochs=1, verbose=0):
self.model.fit(x, y, batch_size=64, epochs=epochs, verbose=verbose)
def predict(self, s, target=False):
if target:
return self.model_.predict(s)
else:
return self.model.predict(s)
def predictOne(self, s, target=False):
return self.predict(s.reshape(1, self.stateCnt), target=target).flatten()
def updateTargetModel(self):
self.model_.set_weights(self.model.get_weights())
#-------------------- MEMORY --------------------------
class Memory: # stored as ( s, a, r, s_ )
samples = []
def __init__(self, capacity):
self.capacity = capacity
def add(self, sample):
self.samples.append(sample)
if len(self.samples) > self.capacity:
self.samples.pop(0)
def sample(self, n):
n = min(n, len(self.samples))
return random.sample(self.samples, n)
def isFull(self):
return len(self.samples) >= self.capacity
#-------------------- AGENT ---------------------------
MEMORY_CAPACITY = 100000
BATCH_SIZE = 64
GAMMA = 0.99
MAX_EPSILON = 1
MIN_EPSILON = 0.01
LAMBDA = 0.001 # speed of decay
UPDATE_TARGET_FREQUENCY = 1000
class Agent:
steps = 0
epsilon = MAX_EPSILON
def __init__(self, stateCnt, actionCnt):
self.stateCnt = stateCnt
self.actionCnt = actionCnt
self.brain = Brain(stateCnt, actionCnt)
self.memory = Memory(MEMORY_CAPACITY)
def act(self, s):
if random.random() < self.epsilon:
return random.randint(0, self.actionCnt-1)
else:
return numpy.argmax(self.brain.predictOne(s))
def observe(self, sample): # in (s, a, r, s_) format
self.memory.add(sample)
if self.steps % UPDATE_TARGET_FREQUENCY == 0:
self.brain.updateTargetModel()
# debug the Q function in poin S
if self.steps % 100 == 0:
S = numpy.array([-0.01335408, -0.04600273, -0.00677248, 0.01517507])
pred = agent.brain.predictOne(S)
print(pred[0])
sys.stdout.flush()
# slowly decrease Epsilon based on our eperience
self.steps += 1
self.epsilon = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON) * math.exp(-LAMBDA * self.steps)
def replay(self):
batch = self.memory.sample(BATCH_SIZE)
batchLen = len(batch)
no_state = numpy.zeros(self.stateCnt)
states = numpy.array([ o[0] for o in batch ])
states_ = numpy.array([ (no_state if o[3] is None else o[3]) for o in batch ])
p = agent.brain.predict(states)
p_ = agent.brain.predict(states_, target=True)
x = numpy.zeros((batchLen, self.stateCnt))
y = numpy.zeros((batchLen, self.actionCnt))
for i in range(batchLen):
o = batch[i]
s = o[0]; a = o[1]; r = o[2]; s_ = o[3]
t = p[i]
if s_ is None:
t[a] = r
else:
t[a] = r + GAMMA * numpy.amax(p_[i])
x[i] = s
y[i] = t
self.brain.train(x, y)
class RandomAgent:
memory = Memory(MEMORY_CAPACITY)
def __init__(self, actionCnt):
self.actionCnt = actionCnt
def act(self, s):
return random.randint(0, self.actionCnt-1)
def observe(self, sample): # in (s, a, r, s_) format
self.memory.add(sample)
def replay(self):
pass
#-------------------- ENVIRONMENT ---------------------
class Environment:
def __init__(self, problem):
self.problem = problem
self.env = gym.make(problem)
def run(self, agent):
s = self.env.reset()
R = 0
while True:
# self.env.render()
a = agent.act(s)
s_, r, done, info = self.env.step(a)
if done: # terminal state
s_ = None
agent.observe( (s, a, r, s_) )
agent.replay()
s = s_
R += r
if done:
break
# print("Total reward:", R)
#-------------------- MAIN ----------------------------
PROBLEM = 'CartPole-v0'
env = Environment(PROBLEM)
stateCnt = env.env.observation_space.shape[0]
actionCnt = env.env.action_space.n
agent = Agent(stateCnt, actionCnt)
randomAgent = RandomAgent(actionCnt)
try:
while randomAgent.memory.isFull() == False:
env.run(randomAgent)
agent.memory.samples = randomAgent.memory.samples
randomAgent = None
while True:
env.run(agent)
finally:
agent.brain.model.save("cartpole-dqn.h5")
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