def test_double_dqn():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1, )))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
memory = SequentialMemory(limit=1000, window_length=1)
policy = EpsGreedyQPolicy(eps=.1)
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=50,
target_model_update=1e-1,
policy=policy,
enable_double_dqn=True)
dqn.compile(Adam(lr=1e-3))
dqn.fit(env, nb_steps=2000, visualize=False, verbose=0)
policy.eps = 0.
h = dqn.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
def test_sarsa():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1,)))
model.add(Activation('relu'))
model.add(Dense(nb_actions, activation='linear'))
policy = EpsGreedyQPolicy(eps=.1)
sarsa = SARSAAgent(model=model, nb_actions=nb_actions, nb_steps_warmup=50, policy=policy)
sarsa.compile(Adam(lr=1e-3))
sarsa.fit(env, nb_steps=20000, visualize=False, verbose=0)
policy.eps = 0.
h = sarsa.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
def test_sarsa():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1,)))
model.add(Activation('relu'))
model.add(Dense(nb_actions, activation='linear'))
policy = EpsGreedyQPolicy(eps=.1)
sarsa = SarsaAgent(model=model, nb_actions=nb_actions, nb_steps_warmup=50, policy=policy)
sarsa.compile(Adam(lr=1e-3))
sarsa.fit(env, nb_steps=20000, visualize=False, verbose=0)
policy.eps = 0.
h = sarsa.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
def test_duel_dqn():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1,)))
model.add(Activation('relu'))
model.add(Dense(nb_actions, activation='linear'))
memory = SequentialMemory(limit=1000, window_length=1)
policy = EpsGreedyQPolicy(eps=.1)
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=50,
target_model_update=1e-1, policy=policy, enable_double_dqn=False, enable_dueling_network=True)
dqn.compile(Adam(lr=1e-3))
dqn.fit(env, nb_steps=2000, visualize=False, verbose=0)
policy.eps = 0.
h = dqn.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
policy=policy,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=400,
target_model_update=1e-1,
enable_double_dqn=True,
enable_dueling_network=True)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
outputFile = open("2014.csv", "w+")
outputFile.write(
"iteration,trainAccuracy,trainCoverage,trainReward,validationAccuracy,validationCoverage,validationReward\n"
)
iteration = 0
policy.eps = 0.1
for i in range(0, 100):
dqn.fit(trainEnv,
nb_steps=3000,
visualize=False,
callbacks=[trainer],
verbose=0)
(episodes, trainCoverage, trainAccuracy, trainReward) = trainer.getInfo()
dqn.test(validationEnv,
nb_episodes=300,
verbose=0,
callbacks=[validator],
visualize=False)
(episodes, validCoverage, validAccuracy, validReward) = validator.getInfo()
outputFile.write(
str(iteration) + "," + str(trainAccuracy) + "," + str(trainCoverage) +
enable_double_dqn=False,
memory=memory,
policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
#dqn.load_weights("Q.weights")
bot.send_message(chat_id=telegramChatID,
text="Experiment started - " +
datetime.datetime.now().strftime("%H:%M"))
percIncrement = 100 / epochs
perc = 0
for i in range(epochs):
policy.eps = 1
dqn.fit(trainEnv, nb_steps=10000, visualize=False, verbose=0)
dqn.test(testEnv, nb_episodes=20, verbose=0, visualize=False)
perc += percIncrement
try:
bot.send_message(chat_id=telegramChatID,
text=str(perc) + " % - " +
datetime.datetime.now().strftime("%H:%M"))
except:
print(str(perc) + " % - " + datetime.datetime.now().strftime("%H:%M"))
trainEnv.changeOutput("walks/train/walk" + str(i + 1) + ".csv")
testEnv.changeOutput("walks/test/walk" + str(i + 1) + ".csv")
dqn.save_weights("Q.weights", overwrite=True)
bot.send_message(chat_id=telegramChatID,