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#We first import the necessary libraries and define hyperparameters -
import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter
LR = 2.33e-4
env = gym.make("CartPole-v0")
observation = env.reset()
goal_steps = 500
score_requirement = 50
initial_games = 10000
#Now we will define a function to generate training data -
def initial_population():
# [OBS, MOVES]
training_data = []
# all scores:
scores = []
# scores above our threshold:
accepted_scores = []
# number of episodes
for _ in range(initial_games):
score = 0
# moves specifically from this episode:
episode_memory = []
# previous observation that we saw
prev_observation = []
for _ in range(goal_steps):
# choose random action left or right i.e (0 or 1)
action = random.randrange(0,2)
observation, reward, done, info = env.step(action)
# since that the observation is returned FROM the action
# we store previous observation and corresponding action
if len(prev_observation) > 0 :
episode_memory.append([prev_observation, action])
prev_observation = observation
score+=reward
if done: break
# reinforcement methodology here.
# IF our score is higher than our threshold, we save
# all we're doing is reinforcing the score, we're not trying
# to influence the machine in any way as to HOW that score is
# reached.
if score >= score_requirement:
accepted_scores.append(score)
for data in episode_memory:
# convert to one-hot (this is the output layer for our neural network)
if data[1] == 1:
output = [0,1]
elif data[1] == 0:
output = [1,0]
# saving our training data
training_data.append([data[0], output])
# reset env to play again
env.reset()
# save overall scores
scores.append(score)
# Now using tflearn we will define our neural network
def neural_network_model(input_size):
network = input_data(shape=[None, input_size, 1], name='input')
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(network, tensorboard_dir='log')
return model
#It is time to train the model now -
def train_model(training_data, model=False):
X = np.array([i[0] for i in training_data]).reshape(-1,len(training_data[0][0]),1)
y = [i[1] for i in training_data]
if not model:
model = neural_network_model(input_size = len(X[0]))
model.fit({'input': X}, {'targets': y}, n_epoch=5, snapshot_step=500, show_metric=True, run_id='openai_CartPole')
return model
training_data = initial_population()
model = train_model(training_data)
#Training complete, now we should play the game to see how the output looks like
scores = []
choices = []
for each_game in range(10):
score = 0
game_memory = []
prev_obs = []
env.reset()
for _ in range(goal_steps):
env.render()
if len(prev_obs)==0:
action = random.randrange(0,2)
else:
action = np.argmax(model.predict(prev_obs.reshape(-1,len(prev_obs),1))[0])
choices.append(action)
new_observation, reward, done, info = env.step(action)
prev_obs = new_observation
game_memory.append([new_observation, action])
score+=reward
if done: break
scores.append(score)
print('Average Score:',sum(scores)/len(scores))
print('choice 1:{} choice 0:{}'.format(float((choices.count(1))/float(len(choices)))*100,float((choices.count(0))/float(len(choices)))*100))
print(score_requirement)
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