plt.title("Location Graph")
for n in range(env.num_vehicles):
if (n < env.num_leading_cars):
plt.plot(np.array(data_d)[:, n] + start_disp[n * 3 + 1], color='b')
elif (n == env.num_leading_cars):
plt.plot(np.array(data_d)[:, n] + start_disp[n * 3 + 1], "g")
else:
plt.plot(np.array(data_d)[:, n] + start_disp[n * 3 + 1], "r")
plt.ylabel("Location")
plt.xlabel("Time")
plt.show()
# CAV Simulator (Generates Fake Data now)
env = Simulator(num_leading_vehicle, num_following_vehicle)
env.normalize = False
#env.verbose = True
num_episodes = num_eps
rewards = []
for i in range(num_episodes):
#
data_t = []
data_d = []
start_disp = None
#
s = env.reset()
#
env.normalize = True
start_disp = env.center_state(env.current_states[0])
torch.save(agent.state_dict(),
'./cem_cartpole.pth') # Path to save model to
print('Episode {}\tBest Average Score: {:.2f}'.\
format(i_iter, np.mean(scores_deque)))
print('Episode {}\tAll Average Score: {:.2f}\tAll SE Score: {:.2f}'.\
format(i_iter, np.mean(rewards), np.std(rewards)/(len(rewards)**0.5)))
return agent, scores
if __name__ == "__main__":
# Variable to designate train or just load from path and test
train = True
#
env = Simulator(num_leading_vehicle, num_following_vehicle)
print('observation space:', env.observation_space)
print('action space:', env.action_space)
agent = Agent(env)
#
if start_from_init:
print("Started from CACC initialization")
agent.load_state_dict(torch.load('./mimic_cav_90_.pth'))
#
if train:
agent, scores = cem(agent)
# evaluate
# load the weights from file
loss.backward()
optimizer.step() # Does the update
print("Loss", loss)
acc = (((torch.argmax(F.softmax(output), 1, keepdim=True)
== target).float().sum()).numpy() / (len(target))) * 100
print("Accuracy", str(acc) + "%")
#print(random.sample([x[1] for x in batch],10))
data_loss.append(loss.detach().numpy())
data_acc.append(acc / 100)
return loss.detach().numpy(), acc
if __name__ == "__main__":
env = Simulator(num_leading_vehicle, num_following_vehicle)
print('observation space:', env.observation_space)
print('action space:', env.action_space)
agent = Agent(env)
# evaluate
# load the weights from file
#agent.load_state_dict(torch.load('./cem_cartpole.pth'))
#agent.load_state_dict(torch.load('./cem_cartpole_5.pth')) # Path to load model from
#agent.load_state_dict(torch.load('./cem_cartpole.pth'))
num_episodes = 1000
rewards = []
Replay_Buffer = deque(maxlen=10000)
def create_loc_map(env):
plt.title("Location Graph")
for n in range(env.num_vehicles):
if(n < env.num_leading_cars):
plt.plot(np.array(data_d)[:,n] + start_disp[n*3 + 1], color='b')
elif(n == env.num_leading_cars):
plt.plot(np.array(data_d)[:,n] + start_disp[n*3 + 1],"g")
else:
plt.plot(np.array(data_d)[:,n] + start_disp[n*3 + 1],"r")
plt.ylabel("Location")
plt.xlabel("Time")
plt.show()
env = Simulator(num_leading_vehicle,num_following_vehicle)
env.normalize = False
#env.verbose = True
num_episodes = num_eps
results = []
for i in range(num_episodes):
#
data_t = []
data_d = []
start_disp = None
#
s = env.reset()
#
env.normalize = True
start_disp = env.center_state(env.current_states[0])
from __future__ import division
# modify from https://github.com/udacity/deep-reinforcement-learning/blob/master/cross-entropy/CEM.ipynb
import numpy as np
import gym
from gym import wrappers
from collections import deque
import torch
import torch.nn as nn
from CAVSimulator0910 import Simulator
env = Simulator(3, 0)
import argparse
import sys
sys.path.append('../../keras-rl')
from PIL import Image
import numpy as np
import gym
from keras.models import Model
from keras.layers import Flatten, Convolution2D, Input, Dense
from keras.optimizers import Adam
import keras.backend as K
from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy, LinearAnnealedPolicy
from rl.memory import SequentialMemory
from rl.core import Processor
from rl.callbacks import TrainEpisodeLogger, ModelIntervalCheckpoint
from keras.models import Sequential
from __future__ import division
# modify from https://github.com/udacity/deep-reinforcement-learning/blob/master/cross-entropy/CEM.ipynb
import numpy as np
import gym
from gym import wrappers
from collections import deque
import torch
import torch.nn as nn
from CAVSimulator0910 import Simulator
num_leading_vehicle = 3
env = Simulator(num_leading_vehicle, 0)
#!/usr/bin/env python
import pickle
import tensorflow as tf
import numpy as np
import tf_util
import gym
def main():
#===========================================================================
# generate expert data
#===========================================================================
# param
envname = 'CAV_Controller'
render = 0