def test_dqn():
args = DQNArgs()
env = gym.make(args.env_name)
agent = DQNAgent(env, QNet, SimpleNormalizer, args)
agent.load(args.save_dir)
for _ in range(10):
agent.test_one_episode(True)
class DQNScheduler:
def __init__(self, simulator):
self.agent = DQNAgent(25, 6)
self.agent.load("./save/car-100-dqn.h5")
self.simulator = simulator
self.agent.epsilon = 0
def schedule(self):
action = self.agent.act(np.reshape(self.simulator.get_state(),
[1, 25]))
return action
def load_model(MODEL_TYPE):
curr_model = None
if MODEL_TYPE == "SVM":
print("LOADING SVM...")
curr_model = load("svm.joblib")
elif MODEL_TYPE == "LR":
print("LOADING LR...")
lr = LogReg(74) #(env.matches.shape[1])
lr.load_weights("weights/weights-improvement-100-0.31.hdf5")
curr_model = lr
elif MODEL_TYPE == "DT":
print("LOADING DT...")
curr_model = load("dt.joblib")
elif MODEL_TYPE == "GB":
print("LOADING GB...")
curr_model = load("gb.joblib")
elif MODEL_TYPE == "RF":
print("LOADING RF...")
curr_model = load("rfc.joblib")
elif MODEL_TYPE == "NB":
print("LOADING NB...")
curr_model = load("nb.joblib")
elif MODEL_TYPE == "AB":
print("LOADING AB...")
curr_model = load("ab.joblib")
elif MODEL_TYPE == "DQN":
print("LOADING DQN...")
BetNet = DQNAgent(75)
BetNet.load("weights/betnet-weights-dqn.h5")
curr_model = BetNet
else:
print("LOADING NN...")
BetNet = Network(74) #(env.matches.shape[1])
BetNet.load_weights(
'weights/Adadelta/test9_400_Best/weights-improvement-400-0.48.hdf5'
) #PCA("weights/Adadelta/test13_100iter_reglast2/weights-improvement-100-0.52.hdf5") # Most recent weights
curr_model = BetNet
return curr_model
def simulateGustsControl(self):
'''
Simulate the response of the controller to gusts.
:return: A plot of the simulation.
'''
self.sim_time = 100
agent = DQNAgent(self.mdp.size, self.action_size)
agent.load(self.src)
WH = self.wh.generateWind()
hdg0 = 0 * TORAD * np.ones(self.wh.samples)
state = self.mdp.initializeMDP(hdg0, WH)
i = np.ones(0)
v = np.ones(0)
wind_heading = np.ones(0)
for time in range(self.sim_time):
WH = self.wh.generateWind()
if time == 20:
WH = self.wh.generateGust(10 * TORAD)
action = agent.actDeterministically(state)
next_state, reward = self.mdp.transition(action, WH)
state = next_state
i = np.concatenate([i, self.mdp.extractSimulationData()[0, :]])
v = np.concatenate([v, self.mdp.extractSimulationData()[1, :]])
wind_heading = np.concatenate([wind_heading, WH[0:10]])
time_vec = np.linspace(0, self.sim_time, int((self.sim_time) / self.mdp.dt))
f, axarr = plt.subplots(2, sharex=True)
axarr[0].plot(time_vec, i / TORAD)
axarr[1].plot(time_vec, v)
axarr[0].set_ylabel("angle of attack")
axarr[1].set_ylabel("v")
plt.show()
def simulateDQNControl(self, hdg0):
'''
Plots the control law of the network over a simulation.
:param hdg0: Initial heading of the boat for the simulation.
:return: A plot of the angle of attack and velocity during the control.
'''
agent = DQNAgent(self.mdp.size, self.action_size)
agent.load(self.src)
WH = self.wh.generateWind()
hdg0 = hdg0 * TORAD * np.ones(self.wh.samples)
state = self.mdp.initializeMDP(hdg0, WH)
i = np.ones(0)
v = np.ones(0)
wind_heading = np.ones(0)
for time in range(self.sim_time):
WH = self.wh.generateWind()
action = agent.actDeterministically(state)
next_state, reward = self.mdp.transition(action, WH)
state = next_state
i = np.concatenate([i, self.mdp.extractSimulationData()[0, :]])
v = np.concatenate([v, self.mdp.extractSimulationData()[1, :]])
wind_heading = np.concatenate([wind_heading, WH[0:10]])
time_vec = np.linspace(0, self.sim_time, int((self.sim_time) / self.mdp.dt))
f, axarr = plt.subplots(2, sharex=True)
axarr[0].plot(time_vec, i / TORAD)
axarr[1].plot(time_vec, v)
axarr[0].set_ylabel("i [°]")
axarr[1].set_ylabel("v [m/s]")
axarr[0].set_xlabel("t [s]")
axarr[1].set_xlabel("t [s]")
plt.show()
elif MODEL_TYPE == "GB":
print("LOADING GB...")
curr_model = load("gb.joblib")
elif MODEL_TYPE == "RF":
print("LOADING RF...")
curr_model = load("rfc.joblib")
elif MODEL_TYPE == "NB":
print("LOADING NB...")
curr_model = load("nb.joblib")
elif MODEL_TYPE == "AB":
print("LOADING AB...")
curr_model = load("ab.joblib")
elif MODEL_TYPE == "DQN":
print("LOADING DQN...")
BetNet = DQNAgent(75)
BetNet.load("weights/betnet-weights-dqn.h5")
curr_model = BetNet
else:
print("LOADING NN...")
BetNet = Network(env.matches.shape[1])
BetNet.load_weights(
"weights/Adadelta/test13_100iter_reglast2/weights-improvement-100-0.52.hdf5"
) # Most recent weights
curr_model = BetNet
###############################################################################
#GETS THE PREDICTION VEC GIVEN MODEL
def generatePrediction(mt, curr_model, to_process):
prediction = None
action_size = 9
actions = [[[0, 0], [-100, -100]], [[0, 0], [-100, 0]], [[0, 0], [-100, 100]],
[[0, 0], [0, -100]], [[0, 0], [0, 0]], [[0, 0], [0, 100]],
[[0, 0], [100, -100]], [[0, 0], [100, 0]], [[0, 0], [100, 100]]]
# actions = [
# [[0,0],[0,0]],
# [[0,0],[0,100]],
# [[0,0],[100,0]],
# [[0,0],[100,100]]
# ]
agent = DQNAgent(state_size, action_size)
#load
agent.load("./save/example_dqn.h5")
done = False
batch_size = 32
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, [1, state_size])
# print(e)
last_reward = 0
for time in range(1000):
# delay.sleep(1/50)
#render
env.render()
# action = agent.act(state)
action = agent.act_2(state)
commands = actions[action]
from keras.optimizers import Adam
from dqn import DQNAgent
from keras.models import model_from_json
from keras.models import load_model
EPISODES = 100
if __name__ == "__main__":
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)
# agent.epsilon = 0.01
# agent.model = model_from_json(open('cartpole.json').read())
agent.load('cp2.h5')
state = env.reset()
state = np.reshape(state, [1, state_size])
done = False
for t in range( 10000 ):
env.render()
# action = agent.act(state)
act_values = agent.model.predict(state)
action = np.argmax(act_values[0]) # returns action
next_state, reward, done, _ = env.step(action)
reward = reward if not done else -10
next_state = np.reshape(next_state, [1, state_size])
state = next_state
state_size = 3
action_size = 9
maxForce = 300
actions = [[[0, 0], [-maxForce, -maxForce]], [[0, 0], [2 * -maxForce, 0]],
[[0, 0], [-maxForce, maxForce]], [[0, 0], [0, 2 * -maxForce]],
[[0, 0], [0, 0]], [[0, 0], [0, 2 * maxForce]],
[[0, 0], [maxForce, -maxForce]], [[0, 0], [2 * maxForce, 0]],
[[0, 0], [maxForce, maxForce]]]
agent = DQNAgent(state_size, action_size)
#load
agent.load("./save/execution1.h5")
env = MyEnvironment(ut=3 / 50)
batch_size = 100
print("done;episode;episodes;score;epsilon")
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, [1, state_size])
# print(e)
last_reward = 0
for time in range(MOVES):
if (not TRAINING):
# delay.sleep(1/50)
env.render()
else:
env.render()
def generateAnimation(self, hdg0):
"""
Generate an animation showing the two Q-values during an interesting control simulation including gusts.
:param hdg0: Initial heading of the boat for the simulation
"""
agent = DQNAgent(self.mdp.size, self.action_size)
agent.load(self.src)
WH = self.wh.generateWind()
hdg0 = hdg0 * TORAD * np.ones(self.wh.samples)
state = self.mdp.initializeMDP(hdg0, WH)
i = np.ones(0)
v = np.ones(0)
NN_Q0 = np.zeros(self.sim_time)
NN_Q1 = np.zeros(self.sim_time)
wind_heading = np.ones(0)
for timesim in range(self.sim_time):
WH = self.wh.generateWind()
if timesim == 50:
WH = self.wh.generateGust(10 * TORAD)
action = agent.actDeterministically(state)
next_state, reward = self.mdp.transition(action, WH)
state = next_state
i = np.concatenate([i, self.mdp.extractSimulationData()[0, :]])
v = np.concatenate([v, self.mdp.extractSimulationData()[1, :]])
NN_Q0[timesim] = self.agent.evaluate(self.mdp.s)[0]
NN_Q1[timesim] = self.agent.evaluate(self.mdp.s)[1]
wind_heading = np.concatenate([wind_heading, WH[0:10]])
time_vec = np.linspace(0, self.sim_time, int((self.sim_time) / self.mdp.dt))
# Visualization tools start here
f = plt.figure(figsize=(15, 5))
ax0 = f.add_subplot(2, 2, 1)
ax1 = f.add_subplot(2, 2, 3)
ax2 = f.add_subplot(2, 2, (2, 4))
ax0.set_title('Simulation')
ax0.set_ylabel('i [°]')
ax0.set_xlabel('t [s]')
ax0.grid(linestyle='-', linewidth=1)
ax1.set_ylabel('v [m/s]')
ax1.set_xlabel('t [s]')
ax1.grid(linestyle='-', linewidth=1)
ax2.set_xticks([0, 1])
ax2.grid(linestyle='-', linewidth=1)
ax2.set_xticklabels(['Bear-off', 'Luff'])
ax2.set_ylim([14, 20])
ax2.set_xlim([0, 1])
ax2.set_title('Q(s,a) of actions')
l0, = ax0.plot(time_vec, i)
l1, = ax1.plot(time_vec, v)
bar0 = ax2.bar([0, 1], [NN_Q0[0], NN_Q1[0]], color=['b', 'r'])
def animate(k):
l0.set_data(time_vec[:k], i[:k])
time.sleep(.0025)
l1.set_data(time_vec[:k], v[:k])
if k % 10 == 0:
kk = k // 10
bar0[0].set_height(NN_Q0[kk])
bar0[1].set_height(NN_Q1[kk])
return l0, l1, bar0
ani = animation.FuncAnimation(f, animate, frames=1000, interval=1, blit=False)
plt.show()
return ani
def generateDeltaAnimation(self, hdg0):
"""
Generate an animation showing the differences between the two Q-values during an interesting control simulation including gusts.
:param hdg0: Initial heading of the boat for the simulation
"""
agent = DQNAgent(self.mdp.size, self.action_size)
agent.load(self.src)
WH = self.wh.generateWind()
hdg0 = hdg0 * TORAD * np.ones(self.wh.samples)
state = self.mdp.initializeMDP(hdg0, WH)
i = np.ones(0)
v = np.ones(0)
NN_Q0 = np.zeros(self.sim_time)
NN_Q1 = np.zeros(self.sim_time)
wind_heading = np.ones(0)
for timesim in range(self.sim_time):
WH = self.wh.generateWind()
if timesim == 50:
WH = self.wh.generateGust(10 * TORAD)
action = agent.actDeterministically(state)
next_state, reward = self.mdp.transition(action, WH)
state = next_state
i = np.concatenate([i, self.mdp.extractSimulationData()[0, :]])
v = np.concatenate([v, self.mdp.extractSimulationData()[1, :]])
NN_Q0[timesim] = self.agent.evaluate(self.mdp.s)[0]
NN_Q1[timesim] = self.agent.evaluate(self.mdp.s)[1]
wind_heading = np.concatenate([wind_heading, WH[0:10]])
time_vec = np.linspace(0, self.sim_time, int((self.sim_time) / self.mdp.dt))
# Visualization tools start here
f = plt.figure(figsize=(15, 5))
ax0 = f.add_subplot(2, 2, 1)
ax1 = f.add_subplot(2, 2, 3)
ax2 = f.add_subplot(2, 2, (2, 4))
ax0.set_title('Simulation')
ax0.set_ylabel('i [°]')
ax0.grid(linestyle='-', linewidth=1)
ax1.set_ylabel('v [m/s]')
ax1.set_xlabel('t [s]')
ax1.grid(linestyle='-', linewidth=1)
ax3 = ax2.twiny()
tresh = np.max(NN_Q0 - NN_Q1)
ax3.plot(np.linspace(-tresh, tresh, 100), 0.5 * np.ones(100)) # Create a dummy plot
ax3.cla()
ax2.set_xticks([-1, 1])
ax2.grid(linestyle='-', linewidth=1)
ax2.set_xticklabels(['Bear off', 'Luff'])
ax2.set_ylim([-.5, .5])
ax2.set_xlim([-1, 1])
ax2.set_title('Q(s,bear-off) - Q(s,luff)', y=-0.1)
ax2.get_yaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
l0, = ax0.plot(time_vec, i / TORAD)
l1, = ax1.plot(time_vec, v)
bar0 = ax2.barh(0, NN_Q1[0] - NN_Q0[0])
ax2.plot([0, 0], [-0.5, 0.5], color='gray')
def animate(k):
l0.set_data(time_vec[:k], i[:k] / TORAD)
time.sleep(.0025)
l1.set_data(time_vec[:k], v[:k])
if k % 10 == 0:
kk = k // 10
bar0[0].set_width(NN_Q1[kk] - NN_Q0[kk])
return l0, l1, bar0
ani = animation.FuncAnimation(f, animate, frames=1000, interval=1, blit=False)
plt.show()
return ani