openaiexample.py

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 mean, median
from collections import Counter

LR = 1e-3
env = gym.make("CartPole-v1")
env.reset()
goal_steps = 500
score_requirement = 50
initial_games = 10000 # might need to modify

def some_random_games_first():
    for episode in range(100):
        env.reset()
        for t in range(goal_steps):
            #env.render()
            action = env.action_space.sample()
            observation, reward, done, info = env.step(action)
            if done:
                break

#some_random_games_first()

def initial_population():
    training_data = []
    scores = []
    accepted_score = []
    for _ in range(initial_games):
        score =0
        game_memory = []
        prev_observation = []
        for _ in range(goal_steps):
            action = random.randrange(0,2)
            observation, reward, done, info = env.step(action)

            if len(prev_observation) > 0:
                game_memory.append([prev_observation,action])
            prev_observation = observation
            score += reward
            if done:
                break
        if score >= score_requirement:
            accepted_score.append(score)
            for data in game_memory:
                if data[1] == 1:
                    output = [0,1]
                elif data[1] == 0:
                    output = [1,0]

                training_data.append([data[0],output])
        env.reset()
        scores.append(score)
    training_data_save = np.array(training_data)
    np.save('saved.npy',training_data_save)

    print('Average accepted score:', mean(accepted_score))
    print('Median accpeted score:',median(accepted_score))
    print(Counter(accepted_score))

    return training_data

def neural_network_model(input_size):
    network = input_data(shape=[None,input_size,1],name = 'input')

    network = fully_connected(network, 128, activation='relu')  # rectified linear
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='relu')  # rectified linear
    network = dropout(network, 0.8)
    network = fully_connected(network, 512, activation='relu')  # rectified linear
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='relu')  # rectified linear
    network = dropout(network, 0.8)
    network = fully_connected(network, 128, activation='relu')  # rectified linear
    network = dropout(network, 0.8)

    network = fully_connected(network,2,activation='softmax') # lookup what softmax is
    network = regression(network,optimizer='adam',learning_rate=LR,
                         loss = 'categorical_crossentropy', name = 'targets')
    model = tflearn.DNN(network,tensorboard_dir='log')

    return model

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=3,snapshot_step=500,show_metric=True,run_id='openaistuff')

    return model



training_data = initial_population()
model = train_model(training_data)
model.save("modeltest.model")

scores = []
choices = []

for each_game in range(20):
    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 2: {}'.format(choices.count(1)/len(choices),
      choices.count(0)/len(choices)))