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task_01.py
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task_01.py
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from scipy import clip, asarray
from pybrain.rl.environments.task import Task
from numpy import *
#Rasberry Pi imports
import RPi.GPIO as GPIO
import time
#Sensors that are used as part of task assesment
GPIO.setmode(GPIO.BOARD)
pir_sensor = 11
def PIR_sensing(pir_sensor):
GPIO.setup(pir_sensor, GPIO.IN)
current_state = 0
time.sleep(0.1)
current_state = GPIO.input(pir_sensor)
#return 1 or 0
if current_state == 1:
return 1
elif current_state == 0:
return 0
class Task(Task):
""" A task is associating a purpose with an environment. It decides how to evaluate the observations, potentially returning reinforcement rewards or fitness values.
Furthermore it is a filter for what should be visible to the agent.
Also, it can potentially act as a filter on how actions are transmitted to the environment. """
def __init__(self, environment):
""" All tasks are coupled to an environment. """
self.env = environment
self.lastreward = 0
# keep last reward. Q lerning reward is given for the interaction before current interaction!
def performAction(self, action):
""" A filtered mapping towards performAction of the underlying environment. """
self.env.performAction(action)
def getObservation(self):
""" A filtered mapping to getSample of the underlying environment. """
sensors = self.env.getSensors()
return sensors
def getReward(self):
""" Compute and return the current reward (i.e. corresponding to the last action performed) """
sensors = self.env.getSensors()#input from environment_01.py
reward = 0
f = 0 #will chang to true (1) to when if statement is fullfilled, otherwise false
print ("Sensor read: ", sensors)
print ("PIR movement: ", PIR_sensing(pir_sensor))
if any(x>=100 for x in sensors) and PIR_sensing(pir_sensor)==1:
# if sensors >= 500 : #works in python2
reward = 1
f = 1
elif any(x>=100 for x in sensors) and PIR_sensing(pir_sensor)==0:
reward = 0
f = 0
elif any(x<=100 for x in sensors) and PIR_sensing(pir_sensor)==0:
reward = 1
f = 1
elif any(x<=100 for x in sensors) and PIR_sensing(pir_sensor)==1:
reward = 0
f = 0
# retrieve last reward - save current received reward
cur_reward = self.lastreward
self.lastreward = reward
print ("Reward: ", cur_reward)
return cur_reward
@property
def indim(self):
return self.env.indim
@property
def outdim(self):
return self.env.outdim