agent.save_weights(args.model, overwrite=True)
# If TEST and TOKEN, submit to crowdAI
if not args.train and args.token:
agent.load_weights(args.model)
# Settings
remote_base = 'http://grader.crowdai.org:1729'
client = Client(remote_base)
# Create environment
observation = client.env_create(args.token)
# Run a single step
# The grader runs 3 simulations of at most 1000 steps each. We stop after the last one
while True:
v = np.array(observation).reshape((env.observation_space.shape[0]))
action = agent.forward(v)
[observation, reward, done, info] = client.env_step(action.tolist())
if done:
observation = client.env_reset()
if not observation:
break
client.submit()
# If TEST and no TOKEN, run some test experiments
if not args.train and not args.token:
agent.load_weights(args.model)
# Finally, evaluate our algorithm for 1 episode.
agent.test(env, nb_episodes=1, visualize=False, nb_max_episode_steps=500)
""" """
memory=memory, nb_steps_warmup_critic=100, nb_steps_warmup_actor=100,
random_process=random_process, gamma=.99, target_model_update=1e-3,
delta_range=(-100., 100.))
# agent = ContinuousDQNAgent(nb_actions=env.noutput, V_model=V_model, L_model=L_model, mu_model=mu_model,
# memory=memory, nb_steps_warmup=1000, random_process=random_process,
# gamma=.99, target_model_update=0.1)
#agent.compile(Adam(lr=.001, clipnorm=1.), metrics=['mae'])
agent.compile([RMSprop(lr=.001), RMSprop(lr=.001)], metrics=['mae'])
# 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.
if args.train:
agent.fit(env, nb_steps=nallsteps, visualize=True, verbose=1, nb_max_episode_steps=env.timestep_limit, log_interval=10000)
# After training is done, we save the final weights.
agent.save_weights(args.output, overwrite=True)
if not args.train:
agent.load_weights(args.output)
# Finally, evaluate our algorithm for 5 episodes.
if args.env != "Arm":
agent.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=500)
else:
for i in range(10000):
if i % 300 == 0:
env.new_target()
print("Target shoulder = %f, elbow = %f" % (env.shoulder,env.elbow))
env.step(agent.forward(env.get_observation()))
target_model_update=1e-3)
agent.compile(optimizer=Adam(
lr=1e-3, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False))
if __name__ == '__main__':
# Load
agent.load_weights('OsmoEnv.h5f')
# Train
# agent.fit(env, nb_steps=50000, verbose=1, nb_max_episode_steps=200)
#
# # Weights
# agent.save_weights('OsmoEnv.h5f',overwrite=True)
#
# # Load
# agent.load_weights('OsmoEnv.h5f')
# Test
# agent.test(env, visualize=False, nb_episodes=50, nb_max_episode_steps=200)
#Play
for _ in range(10):
observation = env.reset()
done = False
while not done:
action = agent.forward(observation)
observation, reward, done, info = env.step(action)
print(action)
else:
print(info)
print("not found weights-file")
rospy.init_node('pub_drive', anonymous=True)
rospy.Subscriber("/observe", Float32MultiArray, callback_observe)
pub = rospy.Publisher('/cmd_vel', Twist, queue_size=1)
r = rospy.Rate(10) # 5hz
fig, ax = plt.subplots(1, 1)
ax.set_ylim(math.radians(-50), math.radians(50))
x = []
y = []
step_count = 0
while not rospy.is_shutdown():
predict_action = agent.forward(observe_data)
pub_vel = Twist()
if predict_action[0] > 0.8:
pub_vel.linear.x = 0.8
elif predict_action[0] < -0.8:
pub_vel.linear.x = -0.8
else:
pub_vel.linear.x = predict_action[0]
if predict_action[1] > 0.8:
pub_vel.angular.z = 0.8
elif predict_action[1] < -0.8:
pub_vel.angular.z = -0.8
else:
pub_vel.angular.z = predict_action[1]
pub_vel.linear.x = pub_vel.linear.x*0.5
memory=memory, nb_steps_warmup_critic=100, nb_steps_warmup_actor=100,
random_process=random_process, gamma=.99, target_model_update=1e-3,
delta_range=(-100., 100.))
# agent = ContinuousDQNAgent(nb_actions=env.noutput, V_model=V_model, L_model=L_model, mu_model=mu_model,
# memory=memory, nb_steps_warmup=1000, random_process=random_process,
# gamma=.99, target_model_update=0.1)
#agent.compile(Adam(lr=.001, clipnorm=1.), metrics=['mae'])
agent.compile([RMSprop(lr=.001), RMSprop(lr=.001)], metrics=['mae'])
# 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.
if args.train:
agent.fit(env, nb_steps=nallsteps, visualize=True, verbose=1, nb_max_episode_steps=env.timestep_limit, log_interval=10000)
# After training is done, we save the final weights.
agent.save_weights(args.output, overwrite=True)
if not args.train:
agent.load_weights(args.output)
# Finally, evaluate our algorithm for 5 episodes.
if args.env != "Arm":
agent.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=500)
else:
for i in range(10000):
if i % 300 == 0:
env.new_target()
print("Target shoulder = %f, elbow = %f" % (env.shoulder,env.elbow))
env.step(agent.forward(env.get_observation()))
agent.save_weights(args.model, overwrite=True)
# If TEST and TOKEN, submit to crowdAI
if not args.train and args.token:
agent.load_weights(args.model)
# Settings
remote_base = 'http://grader.crowdai.org:1729'
client = Client(remote_base)
# Create environment
observation = client.env_create(args.token)
# Run a single step
# The grader runs 3 simulations of at most 1000 steps each. We stop after the last one
while True:
v = np.array(observation).reshape((env.observation_space.shape[0]))
action = agent.forward(v)
[observation, reward, done, info] = client.env_step(action.tolist())
if done:
observation = client.env_reset()
if not observation:
break
client.submit()
# If TEST and no TOKEN, run some test experiments
if not args.train and not args.token:
agent.load_weights(args.model)
# Finally, evaluate our algorithm for 1 episode.
agent.test(env, nb_episodes=1, visualize=False, nb_max_episode_steps=500)
nb_max_episode_steps=1000)
print('Saving model ' + args.model)
agent.save_weights(args.model, overwrite=True)
print('Saved model ' + args.model)
with open(args.model + '_history', 'w') as f:
f.write(str(keras_history.history))
summarize.plot_diagrams(keras_history.history, args.model)
if args.test:
agent.load_weights(args.model)
agent.test(env, nb_episodes=1, nb_max_episode_steps=env.timestep_limit)
if args.submit:
agent.load_weights(args.model)
remote_base = 'http://grader.crowdai.org:1729'
token = '688545d8ba985c174b4f967b40924a43'
client = Client(remote_base)
observation = client.env_create(token)
# The grader runs 3 simulations of at most 1000 steps each. We stop after the last one
while True:
[observation, reward, done,
info] = client.env_step(agent.forward(observation).tolist())
print(observation)
if done:
observation = client.env_reset()
if not observation:
break
client.submit()
# Ctrl + C.
if args.train:
agent.fit(env,
nb_steps=nallsteps,
visualize=True,
verbose=1,
nb_max_episode_steps=env.timestep_limit,
log_interval=10000,
prefix=prefix)
# After training is done, we save the final weights.
agent.save_weights("%s.h5f" % args.output, overwrite=True)
if not args.train:
agent.load_weights("%s.h5f" % args.output)
# Finally, evaluate our algorithm for 5 episodes.
if args.env != "Arm":
agent.test(env,
nb_episodes=5,
visualize=True,
nb_max_episode_steps=500)
else:
for i in range(10000):
if i % 300 == 0:
env.new_target()
print("\n\nTarget shoulder = %f, elbow = %f" %
(env.shoulder, env.elbow))
obs = env.get_observation()
print "Actual shoulder = %f, elbow = %f\r" % (obs[2], obs[3]),
env.step(agent.forward(obs))
env.is_train = False
env.plot_row = 1
env.plot_col = 5
q_values = pd.DataFrame()
st = status.reshape([-1, 1])
for action in actions:
state1_batch_with_action = [
st, np.ones(st.shape).reshape(-1, 1, 1) * action
]
q_values = pd.concat([
q_values,
pd.DataFrame(
agent.target_critic.predict_on_batch(state1_batch_with_action))
],
axis=1)
q_values.to_csv('critic.csv')
with open('actor.json', 'w') as fw:
observation = status.tolist()
action = [float(agent.forward(np.array([obs]))[0]) for obs in observation]
json.dump({'observation': observation, 'action': action}, fw)
agent.test(env,
nb_episodes=5,
visualize=True,
verbose=1,
nb_max_episode_steps=20)
env.plt.ioff()
env.plt.show()
# gamma=.99, target_model_update=0.1)
#agent.compile(Adam(lr=.001, clipnorm=1.), metrics=['mae'])
agent.compile([RMSprop(lr=.001), RMSprop(lr=.001)], metrics=['mae'])
prefix = args.output if args.output else "%s_s%f_t%f" % (args.env ,float(args.sigma), float(args.theta))
# 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.
if args.train:
agent.fit(env, nb_steps=nallsteps, visualize=True, verbose=1, nb_max_episode_steps=env.timestep_limit, log_interval=10000, prefix=prefix)
# After training is done, we save the final weights.
agent.save_weights("%s.h5f" % args.output, overwrite=True)
if not args.train:
agent.load_weights("%s.h5f" % args.output)
# Finally, evaluate our algorithm for 5 episodes.
if args.env != "Arm":
agent.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=500)
else:
for i in range(10000):
if i % 300 == 0:
env.new_target()
print("\n\nTarget shoulder = %f, elbow = %f" % (env.shoulder,env.elbow))
obs = env.get_observation()
print "Actual shoulder = %f, elbow = %f\r" % (obs[2],obs[3]),
env.step(agent.forward(obs))
