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generate_dataset.py
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import numpy as np
import tensorflow as tf
import gym
import os
import mission_control_breakout as mc
import ops
import matplotlib.pyplot as plt
from gym.wrappers import Monitor
# Setup the environment
env = gym.make('BreakoutDeterministic-v4')
# env = Monitor(env=env, directory="./Results/Videos/Breakout", resume=True)
# Placeholders
X_input = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 4], name='Observations')
Y_target = tf.placeholder(dtype=tf.float32, shape=[None, 4], name='Target_Q_values')
def get_agent(x, reuse=False):
"""
Generate the CNN agent
:param x: tensor, Input frames concatenated along axis 3
:param reuse: bool, True -> Reuse weight variables
False -> Create new ones
:return: Tensor, logits for each valid action
"""
if reuse:
tf.get_variable_scope().reuse_variables()
x = tf.divide(x, 255.0, name='Normalize')
conv_1 = tf.nn.relu(ops.cnn_2d(x, weight_shape=mc.conv_1, strides=mc.stride_1, name='conv_1'))
conv_2 = tf.nn.relu(ops.cnn_2d(conv_1, weight_shape=mc.conv_2, strides=mc.stride_2, name='conv_2'))
conv_3 = tf.nn.relu(ops.cnn_2d(conv_2, weight_shape=mc.conv_3, strides=mc.stride_3, name='conv_3'))
conv_3_r = tf.reshape(conv_3, [-1, 7 * 7 * 64], name='reshape')
dense_1 = tf.nn.relu(ops.dense(conv_3_r, 7 * 7 * 64, mc.dense_1, name='dense_1'))
output = ops.dense(dense_1, mc.dense_1, mc.dense_2, name='dense_2')
return output
def make_directories():
"""
Create directories to store tenorboard files, saved models and log files during each unique run.
:param main_dir: String, points to a results file
:return: list of strings, required directories paths
"""
main_dir = "./Dataset/Breakout"
train_dir = main_dir + "/train"
test_dir = main_dir + "/test"
os.mkdir(main_dir)
os.mkdir(train_dir)
os.mkdir(test_dir)
return main_dir, train_dir, test_dir
def play_n_collect(sess, agent, no_plays, log_dir=None, show_ui=False, show_action=False):
"""
Use a trained agent to play a required number of games
:param sess: op, session instance from tensorflow
:param agent: tensor, trained agent structure/graph
:param no_plays: int, you get it
:param log_dir: string, place to store the log files during gameplay
:param show_ui: bool, True -> Show game screen
False -> Should I explain this?
:param show_action: bool, True -> Show the actions taken by the trained agent
False -> Hmm, what can this be?
:return: just prints the results with nothing being returned
"""
rewards = []
main_dir, train_dir, test_dir = make_directories()
step = 0
for p in range(no_plays):
frame = 0
observation = env.reset()
if p < 1000:
# Save the first image
episode_path = train_dir + "/{:05d}".format(p)
else:
episode_path = test_dir + "/{:05d}".format(p % 1000)
os.mkdir(episode_path)
plt.imsave(arr=observation, fname=episode_path + "/{:06d}.png".format(frame))
observation = ops.convert_to_gray_n_resize(observation)
observation = np.expand_dims(observation, axis=2)
state = np.repeat(observation, 4, axis=2)
state = np.expand_dims(state, axis=0)
done = False
reward = 0
while not done:
if show_ui:
env.render()
if np.random.rand() < 0.07:
action = env.action_space.sample()
else:
action = np.argmax(sess.run(agent, feed_dict={X_input: state}))
# Save the action taken
with open(episode_path + "/action.txt", "a") as log:
log.write("{}\n".format(action))
if show_action:
print(action)
frame += 1
step += 1
new_state, r, done, _ = env.step(action)
plt.imsave(arr=new_state, fname=episode_path + "/{:06d}.png".format(frame))
next_state = ops.convert_to_gray_n_resize(new_state)
next_state = np.expand_dims(next_state, axis=2)
next_state = np.expand_dims(next_state, axis=0)
state = np.append(next_state, state[:, :, :, :3], axis=3)
reward += r
rewards.append(reward)
print("Step: {}/500e3".format(step))
print("Game: {}/{}".format(p + 1, no_plays))
print("Reward: {}\n".format(reward))
if not log_dir is None:
with open(log_dir + "/log.txt", "a") as log_file:
log_file.write("Game: {}/{}\n".format(p + 1, no_plays))
log_file.write("Reward: {}\n".format(reward))
print("------------------------------------------------------------------------------------------------------")
print("Best reward: {}".format(np.amax(rewards)))
print("Average reward: {}".format(np.mean(rewards)))
if not log_dir is None:
with open(log_dir + "/log.txt", "a") as log_file:
log_file.write("Best reward: {}\n".format(np.amax(rewards)))
log_file.write("Average reward: {}\n".format(np.mean(rewards)))
def train():
"""
Trains the agent with hyperparameters and other info loaded from mission_control_<game>.py file
:param train_model: bool, True -> Trains the agent
False -> Loads the LATEST trained agent and plays
:return: absolutely nothing
"""
with tf.variable_scope("Action_agent"):
agent = get_agent(X_input)
saver = tf.train.Saver()
with tf.Session() as sess:
# Get the latest trained model
saved_models = os.listdir(mc.logdir)
latest_saved_model = sorted(saved_models)[-1]
saver.restore(sess, tf.train.latest_checkpoint(mc.logdir + latest_saved_model + "/saved_models/"))
print("Getting model from: {}".format(mc.logdir + latest_saved_model + "/saved_models/"))
print("------------------------Playing----------------------------")
play_n_collect(sess=sess, agent=agent, no_plays=1200, log_dir=None,
show_ui=False, show_action=mc.show_action)
if __name__ == '__main__':
train()