# Make a neural net with 3 hidden layers
def agent(states, actions):
model = Sequential()
model.add(Flatten(input_shape=(1, states)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(actions, activation='linear'))
return model
# Actually make a neural net with 3 hidden layers
model = agent(env.observation_space.shape[0], env.action_space.n)
policy = EpsGreedyQPolicy()
# Create a tensorflow reinforcement learning agent using the [state > action > reward] system
sarsa = SARSAAgent(model=model, policy=policy, nb_actions=env.action_space.n)
# Choose how we calculate reward and modify the model
sarsa.compile('adam', metrics=['mse'])
# sarsa.fit(env, nb_steps = 50000, visualize = False, verbose = 1)
sarsa.load_weights('cartpolekerassarsa.h5f')
scores = sarsa.test(env, nb_episodes=10, visualize=False)
print('Average score over 10 test games: {}'.format(
np.mean(scores.history['episode_reward'])))
sarsa.save_weights('cartpolekerassarsa.h5f', overwrite=True)
sarsa.test(env, nb_episodes=2, visualize=True)
kernel_initializer=weight_initializer)(hiddenLayer)
outputLayer = Dense(nb_actions, activation='linear')(hiddenLayer)
model = Model(inputLayer, outputLayer)
print(model.summary())
# SARSA does not require a memory.
policy = BoltzmannQPolicy()
sarsa = SARSAAgent(model=model,
nb_actions=nb_actions,
nb_steps_warmup=10,
policy=policy)
sarsa.compile(Adam(lr=1e-3), metrics=['mae'])
if loadFromExisting:
sarsa.load_weights(file_path)
else:
startTime = time.time()
sarsa.fit(env, nb_steps=nSteps, visualize=True, verbose=1)
endTime = time.time()
sarsa.save_weights(file_path, overwrite=True)
# After training is done, we save the final weights.
# Finally, evaluate our algorithm for 5 episodes.
sarsa.test(env, nb_episodes=5, visualize=True)
if not loadFromExisting:
print("Time taken to trian: {0}".format(endTime - startTime))
env = gym.make('CartPole-v1')
states = env.observation_space.shape[0]
actions = env.action_space.n
def agent(states, actions):
model = Sequential()
model.add(Flatten(input_shape=(1, states)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(actions, activation='linear'))
return model
model = agent(env.observation_space.shape[0], env.action_space.n)
from rl.agents import SARSAAgent
from rl.policy import EpsGreedyQPolicy
sarsa = SARSAAgent(model=model,
policy=EpsGreedyQPolicy(),
nb_actions=env.action_space.n)
# sarsa.compile('adam', metrics = ['mse'])
# sarsa.fit(env, nb_steps = 50000, visualize = False, verbose = 1)
# scores = sarsa.test(env, nb_episodes = 100, visualize= True)
# sarsa.save_weights('1-sarsa_weights.h5f', overwrite=True)
sarsa.load_weights('1-sarsa_weights.h5f')
_ = sarsa.test(env, nb_episodes=100, visualize=True)
print('Average score over 100 test games:{}'.format(
np.mean(_.history['episode_reward'])))
for key in dc:
re.append(dc[key])
return re
tt = dict_to_list(tpl.rewards_mean)
mm = np.array(tt[:-1])
kk = dict_to_list(tpl.metrics_at_end)
jj = np.array(kk[:-1])
metrics = np.column_stack((mm, jj))
import pickle
pickle.dump(metrics, open('sarsa_%d_%s_metrics.p' % (scale, ENV_NAME), "wb"))
# load model for testing
sarsa.load_weights('/home/am/Desktop/set_tests/final/sarsa_%d_%s_weights.h5f' %
(scale, ENV_NAME))
# setting up monitoring tools to record the testing episodes
from gym import monitoring
from gym.wrappers import Monitor
def episode5(episode_id):
if episode_id < 5:
return True
else:
return False
#rec = StatsRecorder(env,"sarsa_1")
#rec.capture_frame()
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(8))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# SARSA does not require a memory.
policy = BoltzmannQPolicy()
sarsa = SARSAAgent(model=model, nb_actions=nb_actions, nb_steps_warmup=10, policy=policy)
sarsa.compile(Adam(lr=1e-3), metrics=['mae'])
model_fn = 'sarsa_{}_weights.h5f'.format(ENV_NAME);
if os.path.isfile(model_fn):
sarsa.load_weights(model_fn)
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
#sarsa.fit(env, nb_steps=50000,nb_max_episode_steps=500, visualize=False, verbose=2)
# After training is done, we save the final weights.
#sarsa.save_weights('sarsa_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
sarsa.test(env, nb_episodes=5, visualize=True)
class DQN:
def __init__(
self,
env="CartPole-v1",
emulateOculus=True,
visualize=True,
teachingFilesPath=None,
policyValues={
"inner_policy": EpsGreedyQPolicy(),
"attr": "eps",
"value_max": 0.75,
"value_min": .01,
"value_test": .0,
"nb_steps": 50000
},
dobotEmulation=False):
self.policyValues = policyValues
os.environ[
"PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print("physical_devices-------------", len(physical_devices))
tf.config.experimental.set_memory_growth(physical_devices[0], True)
self.episodeLength = 25
if env == "CartPole-v1":
self.env = gym.make('CartPole-v1')
self.states = self.env.observation_space.shape[0]
self.actions = self.env.action_space.n
self.saveFileName = 'sarsa_weights.h5f'
logdir = "logs/CartPoleV1/" + datetime.now().strftime(
"%Y%m%d-%H%M%S")
self.tensorboard_callback = keras.callbacks.TensorBoard(
log_dir=logdir)
self.visualize = True
elif env == "Dobot":
self.env = dobotGym.dobotGym(emulateOculus=emulateOculus,
episodeLength=self.episodeLength,
visualize=visualize,
teachingFilesPath=teachingFilesPath,
dobotEmulation=dobotEmulation)
self.states = self.env.observation_space.shape[0]
self.actions = self.env.action_space.shape[0]
self.saveFileName = 'sarsa_weights_dobot.h5f'
logdir = "logs/Dobot/" + datetime.now().strftime("%Y%m%d-%H%M%S")
self.tensorboard_callback = keras.callbacks.TensorBoard(
log_dir=logdir)
self.visualize = True
else:
raise TypeError("Wrong env")
print(
'States', self.states
) # To get an idea about the number of variables affecting the environment
print(
'Actions', self.actions
) # To get an idea about the number of possible actions in the environment, do [right,left]
#
# episodes = 10
# for episode in range(1, episodes + 1):
# # At each begining reset the game
# state = self.env.reset()
# # set done to False
# done = False
# # set score to 0
# score = 0
# # while the game is not finished
# while not done:
# # visualize each step
# self.env.render()
# # choose a random action
# action = random.choice([0, 1])
# # execute the action
# n_state, reward, done, info = self.env.step(action)
# # keep track of rewards
# score += reward
# print('episode {} score {}'.format(episode, score))
# not working :(
# self.agent = self.agentDDP(self.states, self.actions)
# self.agent = self.NAFAgent(self.states, self.actions)
# self.policy = EpsGreedyQPolicy()
self.savingFreq = 100
self.actualSaving = 0
self.model = self.agentSarsa(self.states, self.actions)
self.policy = LinearAnnealedPolicy(
inner_policy=self.policyValues["inner_policy"],
attr=self.policyValues["attr"],
value_max=self.policyValues["value_max"],
value_min=self.policyValues["value_min"],
value_test=self.policyValues["value_test"],
nb_steps=self.policyValues["nb_steps"])
self.agent = SARSAAgent(model=self.model,
policy=self.policy,
nb_actions=self.actions)
self.agent._is_graph_network = True
def t():
return False
self.agent._in_multi_worker_mode = t
self.agent.save = self.saveAgentWeights
def lenmeh():
return self.actions
# self.agent.__len__ = lenmeh
def saveAgentWeights(self, path, overwrite=True):
if self.actualSaving < self.savingFreq:
self.actualSaving += 1
return None
else:
self.actualSaving = 0
path = 'model/checkpoint/' + datetime.now().strftime(
"%Y%m%d-%H%M%S") + self.saveFileName
self.agent.save_weights(path, overwrite)
def agentSarsa(self, states, actions):
self.model = Sequential()
self.model.add(LSTM(42, activation='sigmoid', input_shape=(1, states)))
self.model.add(Dense(42, activation='sigmoid'))
self.model.add(Dense(42, activation='sigmoid'))
self.model.add(Dense(24, activation='sigmoid'))
self.model.add(Dense(12, activation='sigmoid'))
self.model.add(Dense(actions, activation='linear'))
self.path = fileOperation.saveToFolder(self.model.to_json(),
name='modelShape',
folder="model\\checkpoint")
# , stateful=False states are resetted together after each batch.
# model.add(Flatten(input_shape=(1, states)))
# dot_img_file = '/model_1.png'
# keras.utils.plot_model(self.model, to_file=dot_img_file, show_shapes=True)
# model.reset_states()
return self.model
def load(self):
path = fileOperation.openDialogFunction(".h5f")
self.agent.compile('adam', metrics=['mse'])
self.agent.load_weights(path)
self.agent.compile('adam', metrics=['mse'])
def test(self, nb_episodes=2):
_ = self.agent.test(self.env,
nb_episodes=nb_episodes,
visualize=self.visualize)
def fit(self, visualize=False):
checkpoint_filepath = 'model/checkpoint/'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=False,
save_freq=25)
self.agent.compile('adam', metrics=['mse'])
self.agent.fit(
self.env,
nb_steps=self.policyValues["nb_steps"],
log_interval=self.episodeLength,
visualize=visualize,
verbose=1,
nb_max_start_steps=1,
start_step_policy=self.model.reset_states,
# callbacks=[PlotLossesKeras()])
callbacks=[self.tensorboard_callback, model_checkpoint_callback],
)
scores = self.agent.test(self.env, nb_episodes=5, visualize=visualize)
print('Average score over 5 test games:{}'.format(
np.mean(scores.history['episode_reward'])))
verbose=2,
nb_max_episode_steps=500,
callbacks=[tb]) # 20s episodes
# print history
print("history contents : ",
hist.history.keys()) # episode_reward, nb_episode_steps, nb_steps
# summarize history for accuracy
import matplotlib.pyplot as plt
plt.plot(hist.history['episode_reward'])
plt.plot(hist.history['nb_episode_steps'])
plt.title('learning')
plt.xlabel('episode')
plt.legend(['episode_reward', 'nb_episode_steps'], loc='upper left')
plt.show()
# save history
with open('_experiments/history_' + filename + '.pickle', 'wb') as handle:
pickle.dump(hist.history, handle, protocol=pickle.HIGHEST_PROTOCOL)
# After training is done, we save the final weights.
sarsa.save_weights('h5f_files/dqn_{}_weights.h5f'.format(filename),
overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
sarsa.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=500)
if mode == 'test':
sarsa.load_weights('h5f_files/dqn_{}_weights.h5f'.format(filename))
sarsa.test(env, nb_episodes=10, visualize=True,
nb_max_episode_steps=400) # 40 seconds episodes
nb_steps_warmup=3,
policy=policy)
agent.compile(Adam(lr=1e-3), metrics=['mae'])
agent.reset_states()
#=========================================================================#
# re-use weights if possible
if (os.path.isfile(inv_weights_fname)):
inverse_model.load_weights(inv_weights_fname)
if (os.path.isfile(fwd_weights_fname)):
forward_model.load_weights(fwd_weights_fname)
if (os.path.isfile(agent_weights_fname)):
agent.load_weights(agent_weights_fname)
# else:
# FIXME: this bit is necessary or agent does nothing???
# probably initializes values or something
# agent.fit(env, nb_steps=20, visualize=False)
agent.training = True # IMPORTANT!!! or it doesn't learn
#=========================================================================#
episode_count = 1000
reward = 0
done = False
for i in range(episode_count):
print "episode=%d" % i
obs_now = env.reset()
#memory = SequentialMemory(limit=50000, window_length=1)
policy = EpsGreedyQPolicyC4(env)
# dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
# target_model_update=1e-2, policy=policy,test_policy=policy)
# dqn.compile(Adam(lr=1e-3), metrics=['mae'])
sarsa = SARSAAgent(model=model,
nb_actions=nb_actions,
nb_steps_warmup=10,
policy=policy,
test_policy=policy)
sarsa.compile(Adam(lr=1e-3), metrics=['mae'])
# Load weights
try:
#dqn.load_weights(weights_filename)
sarsa.load_weights(weights_filename)
except OSError:
print("no saved weights found")
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
#dqn.fit(env, nb_steps=5000000, visualize=False, verbose=2)
sarsa.fit(env,
nb_steps=50000,
visualize=False,
verbose=1,
callbacks=[WandbCallback()])
# After training is done, we save the final weights.
#dqn.save_weights('dqn_{}_weights.h5f'.format(ENV_NAME), overwrite=True)