#rendering the model
from ped_car_2 import PedestrianEnv
import numpy as np
import random
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from keras.models import load_model
from My_DDQN import DDQN
env = PedestrianEnv()
observation = env.reset()
observation_space = len(
observation) #we get the number of parameters in the state
action_space = 10 #number of discrete velocities pedestrian can take
agent = DDQN(observation_space, action_space)
agent.exploration_rate = 0
agent.model = load_model('ddqn_ped_Learner.h5')
death_toll = 0
safe_chicken = 0
done_count = 0
count = 0
Ped_Pos = []
Car_xPos = []
Car_yPos = []
d = env.d
W = env.W
env = PedestrianEnv()
episodes = 3
for e in range(episodes):
state = env.reset()
state = np.reshape(state, [1, observation_space])
#rendering the model
from ped_car_v11 import PedestrianEnv
import numpy as np
import random
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from keras.models import load_model
from My_DDQN import DDQN
env = PedestrianEnv()
observation = env.reset()
observation_space = len(
observation) #we get the number of parameters in the state
action_space = 8 #number of discrete velocities pedestrian can take
agent = DDQN(observation_space, action_space)
agent.exploration_rate = 0
agent.model = load_model('ddqn_ped_v11.h5')
death_toll = 0
safe_chicken = 0
done_count = 0
count = 0
Ped_Pos = []
Car_xPos = []
Car_yPos = []
d = env.d
W = env.W
env = PedestrianEnv()
episodes = 3
for e in range(episodes):
state = env.reset()
state = np.reshape(state, [1, observation_space])
#rendering the model
from ped_car_v11 import PedestrianEnv
import numpy as np
import random
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from keras.models import load_model
from My_DDQN import DDQN
env = PedestrianEnv()
observation = env.reset(np.random.randint(1, 5))
observation_space = len(
observation) #we get the number of parameters in the state
action_space = 3 #number of discrete velocities pedestrian can take
agent = DDQN(observation_space, action_space)
agent.exploration_rate = 0
agent.model = load_model('ddqn_ped_APPROX.h5')
death_toll = 0
safe_chicken = 0
done_count = 0
count = 0
Ped_Pos = []
Car_xPos = []
Car_yPos = []
d = env.d
W = env.W
env = PedestrianEnv()
episodes = 100
for e in range(episodes):
state = env.reset(np.random.randint(1, 5))
state = np.reshape(state, [1, observation_space])
#rendering the model
from car_ped_2 import CarEnv
import numpy as np
import random
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from keras.models import load_model
from My_DDQN import DDQN
env=CarEnv()
observation = env.reset()
observation_space=len(observation) #we get the number of parameters in the state
action_space=10 #number of discrete velocities pedestrian can take
agent=DDQN(observation_space, action_space)
agent.exploration_rate=0
agent.model=load_model('ddqn_car_Learner_1.h5')
death_toll=0
safe_chicken=0
safe_car=0
done_count=0
count=0
Ped_Pos=[]
Car_xPos=[]
Car_yPos=[]
d = env.d
W = env.W
env = CarEnv()
episodes = 3
for e in range(episodes):
state=env.reset()
state = np.reshape(state, [1, observation_space])