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import random
import numpy as np
import gym
import time
from tqdm import tqdm
import configparser
class Qlearning:
###########################################################################
# START - __init__ function
###########################################################################
# INPUTS:
# env - Cart Pole environment
# alpha - step size
# gamma - discount rate
# epsilon - parameter for epsilon-greedy approach
# numberEpisodes - total number of simulation episodes
# numberOfBins - this is a 4 dimensional list that defines the number of grid points
# for state discretization
# that is, this list contains number of bins for every state entry,
# we have 4 entries, that is,
# discretization for cart position, cart velocity, pole angle, and pole angular velocity
# lowerBounds - lower bounds (limits) for discretization, list with 4 entries:
# lower bounds on cart position, cart velocity, pole angle, and pole angular velocity
# upperBounds - upper bounds (limits) for discretization, list with 4 entries:
# upper bounds on cart position, cart velocity, pole angle, and pole angular velocity
def __init__(self, env = gym.make('CartPole-v1'), file='config.ini'):
self.env = env
self.load_values(file)
def load_values(self,file):
config = configparser.ConfigParser()
config.read(file)
cart_velocity_min = float(config['Parameters']['cart_velocity_min'])
cart_velocity_max = float(config['Parameters']['cart_velocity_max'])
pole_angle_velocity_min = float(config['Parameters']['pole_angle_velocity_min'])
pole_angle_velocity_max = float(config['Parameters']['pole_angle_velocity_max'])
number_of_bins_position = int(config['Parameters']['number_of_bins_position'])
number_of_bins_velocity = int(config['Parameters']['number_of_bins_velocity'])
number_of_bins_angle = int(config['Parameters']['number_of_bins_angle'])
number_of_bins_angle_velocity = int(config['Parameters']['number_of_bins_angle_velocity'])
self.action_number = self.env.action_space.n
self.alpha = float(config['Parameters']['alpha'])
self.gamma = float(config['Parameters']['gamma'])
self.epsilon = float(config['Parameters']['epsilon'])
self.numEpisodes = int(config['Parameters']['number_episodes'])
self.upperBounds = self.env.observation_space.high
self.lowerBounds = self.env.observation_space.low
self.upperBounds[1] = cart_velocity_max
self.upperBounds[3] = pole_angle_velocity_max
self.lowerBounds[1] = cart_velocity_min
self.lowerBounds[3] = pole_angle_velocity_min
self.batch_size = int(config['Parameters']['batch_size'])
self.rewardsEpisode = 0
self.sumRewardsEpisode = []
# Update the number of bins
self.num_bins = [number_of_bins_position, number_of_bins_velocity, number_of_bins_angle,
number_of_bins_angle_velocity]
self.replayBuffer = []
self.Q = np.random.uniform(0, 1, size=(self.num_bins[0], self.num_bins[1], self.num_bins[2], self.num_bins[3], self.action_number))
# Observation space is not discrete so we make it discrete
def returnIndexState(self, state):
position = state[0]
velocity = state[1]
angle = state[2]
angularVelocity = state[3]
cartPositionBin = np.linspace(self.lowerBounds[0], self.upperBounds[0], self.num_bins[0])
cartVelocityBin = np.linspace(self.lowerBounds[1], self.upperBounds[1], self.num_bins[1])
cartAngleBin = np.linspace(self.lowerBounds[2], self.upperBounds[2], self.num_bins[2])
cartAngularVelocityBin = np.linspace(self.lowerBounds[3], self.upperBounds[3], self.num_bins[3])
indexPosition = np.maximum(np.digitize(position, cartPositionBin) - 1, 0)
indexVelocity = np.maximum(np.digitize(velocity, cartVelocityBin) - 1, 0)
indexAngle = np.maximum(np.digitize(angle, cartAngleBin) - 1, 0)
indexAngularVelocity = np.maximum(np.digitize(angularVelocity, cartAngularVelocityBin) - 1, 0)
return tuple([indexPosition, indexVelocity, indexAngle, indexAngularVelocity])
def selectAction(self, state, index):
# First 10% episodes will be random
if index < self.numEpisodes * 0.1:
return np.random.choice(self.action_number)
# We generate a random number to decide if we are exploring or not.
randomNumber = np.random.random()
# Decay starts at 55%
if index > self.numEpisodes * 0.6:
self.epsilon = 0.999 * self.epsilon
# If satisfied we are exploring
if randomNumber < self.epsilon:
return np.random.choice(self.action_number)
# Else we are being greedy
else:
return np.random.choice(np.where(
self.Q[self.returnIndexState(state)] == np.max(self.Q[self.returnIndexState(state)]))[0])
def train(self):
for indexEpisode in tqdm(range(self.numEpisodes)):#, miniters=1):
#for indexEpisode in range(self.numEpisodes):
rewardsEpisode = []
(stateS, _) = self.env.reset()
stateS = list(stateS)
#print(f'Simulating Episode {indexEpisode}')
terminalState = False
steps = 0
# Add a steps limiter to shorten training time
while not terminalState and steps < 2000:
steps += 1
stateSIndex = self.returnIndexState(stateS)
actionA = self.selectAction(stateS, indexEpisode)
(stateSprime, reward, terminalState, _, _) = self.env.step(actionA)
rewardsEpisode.append(reward)
stateSprime = list(stateSprime)
# Store the experience in the buffer
self.replayBuffer.append([stateS,actionA,reward,stateSprime,terminalState])
stateSprimeIndex = self.returnIndexState(stateSprime)
QmaxPrime = np.max(self.Q[stateSprimeIndex])
if not terminalState:
error = reward + self.gamma * QmaxPrime - self.Q[stateSIndex + (actionA,)]
self.Q[stateSIndex + (actionA,)] = self.Q[stateSIndex + (actionA,)] + self.alpha * error
else:
error = reward - self.Q[stateSIndex + (actionA,)]
self.Q[stateSIndex + (actionA,)] = self.Q[stateSIndex + (actionA,)] + self.alpha * error
stateS = stateSprime
if indexEpisode % 5 == 0:
self.updateQValues()
#print("Sum of rewards {}".format(np.sum(rewardsEpisode)))
self.sumRewardsEpisode.append(np.sum(rewardsEpisode))
def updateQValues(self):
if len(self.replayBuffer)<self.batch_size:
return
# Select a random batch of experiences
batch = random.sample(self.replayBuffer, self.batch_size)
for experience in batch:
state,action,reward,next_state,done = experience
stateIndex = self.returnIndexState(state)
actionIndex = action
if not done:
next_stateIndex = self.returnIndexState(next_state)
QmaxPrime = np.max(self.Q[next_stateIndex])
error = reward + self.gamma * QmaxPrime - self.Q[stateIndex + (actionIndex,)]
else:
error = reward - self.Q[stateIndex + (actionIndex,)]
self.Q[stateIndex + (actionIndex,)] += self.alpha * error
def simulateLearnedStrategy(self,env1 = gym.make("CartPole-v1"), render=False):
import gym
import time
# Choose this line if you want to see how it behaves
#env1 = gym.make("CartPole-v1", render_mode='human')
(currentState, _) = env1.reset()
if render:
env1.render()
timeSteps = 3000
steps = 0
# obtained rewards at every time step
obtainedRewards = []
terminated = False
truncated = False
while (not (terminated or truncated)) or steps < timeSteps:
steps+=1
#print(timeIndex)
# select greedy actions
actionInStateS = np.random.choice(np.where(self.Q[self.returnIndexState(currentState)] == np.max(
self.Q[self.returnIndexState(currentState)]))[0])
currentState, reward, terminated, truncated, info = env1.step(actionInStateS)
obtainedRewards.append(reward)
time.sleep(0.05)
if (terminated):
time.sleep(1)
break
return obtainedRewards, env1
def simulateRandomStrategy(self):
env2 = gym.make('CartPole-v1')
(currentState, _) = env2.reset()
#env2.render()
# number of simulation episodes
episodeNumber = 100
# time steps in every episode
timeSteps = 1000
# sum of rewards in each episode
rewardsEpisode = []
for timeIndex in range(timeSteps):
random_action = env2.action_space.sample()
observation, reward, terminated, truncated, info = env2.step(random_action)
rewardsEpisode.append(reward)
if (terminated):
break
return np.sum(rewardsEpisode), env2
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