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import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import threading
import gym
import multiprocessing
import numpy as np
from queue import Queue
import argparse
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python import keras
from tensorflow.python.keras import layers
parser = argparse.ArgumentParser(description='Run A3C algorithm on the game '
'Cartpole.')
parser.add_argument('--algorithm', default='a3c', type=str,
help='Choose between \'a3c\' and \'random\'.')
parser.add_argument('--train', dest='train', action='store_true',
help='Train our model.')
parser.add_argument('--lr', default=0.001,
help='Learning rate for the shared optimizer.')
parser.add_argument('--update-freq', default=20, type=int,
help='How often to update the global model.')
parser.add_argument('--max-eps', default=1000, type=int,
help='Global maximum number of episodes to run.')
parser.add_argument('--gamma', default=0.99,
help='Discount factor of rewards.')
parser.add_argument('--save-dir', default='/tmp/', type=str,
help='Directory in which you desire to save the model.')
args = parser.parse_args()
class ActorCriticModel(keras.Model):
def __init__(self, state_size, action_size):
super(ActorCriticModel, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.dense1 = layers.Dense(100, activation='relu')
self.policy_logits = layers.Dense(action_size)
self.dense2 = layers.Dense(100, activation='relu')
self.values = layers.Dense(1)
def call(self, inputs):
# Forward pass
x = self.dense1(inputs)
logits = self.policy_logits(x)
v1 = self.dense2(inputs)
values = self.values(v1)
return logits, values
def record(episode,
episode_reward,
worker_idx,
global_ep_reward,
result_queue,
total_loss,
num_steps):
"""Helper function to store score and print statistics.
Arguments:
episode: Current episode
episode_reward: Reward accumulated over the current episode
worker_idx: Which thread (worker)
global_ep_reward: The moving average of the global reward
result_queue: Queue storing the moving average of the scores
total_loss: The total loss accumualted over the current episode
num_steps: The number of steps the episode took to complete
"""
if global_ep_reward == 0:
global_ep_reward = episode_reward
else:
global_ep_reward = global_ep_reward * 0.99 + episode_reward * 0.01
print(
f"Episode: {episode} | "
f"Moving Average Reward: {int(global_ep_reward)} | "
f"Episode Reward: {int(episode_reward)} | "
f"Loss: {int(total_loss / float(num_steps) * 1000) / 1000} | "
f"Steps: {num_steps} | "
f"Worker: {worker_idx}"
)
result_queue.put(global_ep_reward)
return global_ep_reward
class RandomAgent:
"""Random Agent that will play the specified game
Arguments:
env_name: Name of the environment to be played
max_eps: Maximum number of episodes to run agent for.
"""
def __init__(self, env_name, max_eps):
self.env = gym.make(env_name)
self.max_episodes = max_eps
self.global_moving_average_reward = 0
self.res_queue = Queue()
def run(self):
reward_avg = 0
for episode in range(self.max_episodes):
done = False
self.env.reset()
reward_sum = 0.0
steps = 0
while not done:
# Sample randomly from the action space and step
_, reward, done, _ = self.env.step(self.env.action_space.sample())
steps += 1
reward_sum += reward
# Record statistics
self.global_moving_average_reward = record(episode,
reward_sum,
0,
self.global_moving_average_reward,
self.res_queue, 0, steps)
reward_avg += reward_sum
final_avg = reward_avg / float(self.max_episodes)
print("Average score across {} episodes: {}".format(self.max_episodes, final_avg))
return final_avg
class MasterAgent():
def __init__(self):
self.game_name = 'CartPole-v0'
save_dir = args.save_dir
self.save_dir = save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
env = gym.make(self.game_name)
self.state_size = env.observation_space.shape[0]
self.action_size = env.action_space.n
self.opt = tf.compat.v1.train.AdamOptimizer(args.lr, use_locking=True)
print(self.state_size, self.action_size)
self.global_model = ActorCriticModel(self.state_size, self.action_size) # global network
self.global_model(tf.convert_to_tensor(np.random.random((1, self.state_size)), dtype=tf.float32))
def train(self):
if args.algorithm == 'random':
random_agent = RandomAgent(self.game_name, args.max_eps)
random_agent.run()
return
res_queue = Queue()
workers = [Worker(self.state_size,
self.action_size,
self.global_model,
self.opt, res_queue,
i, game_name=self.game_name,
save_dir=self.save_dir) for i in range(multiprocessing.cpu_count())]
for i, worker in enumerate(workers):
print("Starting worker {}".format(i))
worker.start()
moving_average_rewards = [] # record episode reward to plot
while True:
reward = res_queue.get()
if reward is not None:
moving_average_rewards.append(reward)
else:
break
[w.join() for w in workers]
plt.plot(moving_average_rewards)
plt.ylabel('Moving average ep reward')
plt.xlabel('Step')
plt.savefig(os.path.join(self.save_dir,
'{} Moving Average.png'.format(self.game_name)))
plt.show()
def play(self):
env = gym.make(self.game_name).unwrapped
state = env.reset()
model = self.global_model
model_path = os.path.join(self.save_dir, 'model_{}.h5'.format(self.game_name))
print('Loading model from: {}'.format(model_path))
model.load_weights(model_path)
done = False
step_counter = 0
reward_sum = 0
try:
while not done:
env.render(mode='rgb_array')
policy, value = model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
policy = tf.nn.softmax(policy)
action = np.argmax(policy)
state, reward, done, _ = env.step(action)
reward_sum += reward
print("{}. Reward: {}, action: {}".format(step_counter, reward_sum, action))
step_counter += 1
except KeyboardInterrupt:
print("Received Keyboard Interrupt. Shutting down.")
finally:
env.close()
class Memory:
def __init__(self):
self.states = []
self.actions = []
self.rewards = []
def store(self, state, action, reward):
self.states.append(state)
self.actions.append(action)
self.rewards.append(reward)
def clear(self):
self.states = []
self.actions = []
self.rewards = []
class Worker(threading.Thread):
# Set up global variables across different threads
global_episode = 0
# Moving average reward
global_moving_average_reward = 0
best_score = 0
save_lock = threading.Lock()
def __init__(self,
state_size,
action_size,
global_model,
opt,
result_queue,
idx,
game_name='CartPole-v0',
save_dir='/tmp'):
super(Worker, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.result_queue = result_queue
self.global_model = global_model
self.opt = opt
self.local_model = ActorCriticModel(self.state_size, self.action_size)
self.worker_idx = idx
self.game_name = game_name
self.env = gym.make(self.game_name).unwrapped
self.save_dir = save_dir
self.ep_loss = 0.0
def run(self):
total_step = 1
mem = Memory()
while Worker.global_episode < args.max_eps:
current_state = self.env.reset()
mem.clear()
ep_reward = 0.
ep_steps = 0
self.ep_loss = 0
time_count = 0
done = False
while not done:
logits, _ = self.local_model(
tf.convert_to_tensor(current_state[None, :],
dtype=tf.float32))
probs = tf.nn.softmax(logits)
action = np.random.choice(self.action_size, p=probs.numpy()[0])
new_state, reward, done, _ = self.env.step(action)
if done:
reward = -1
ep_reward += reward
mem.store(current_state, action, reward)
if time_count == args.update_freq or done:
# Calculate gradient wrt to local model. We do so by tracking the
# variables involved in computing the loss by using tf.GradientTape
with tf.GradientTape() as tape:
total_loss = self.compute_loss(done,
new_state,
mem,
args.gamma)
self.ep_loss += total_loss
# Calculate local gradients
grads = tape.gradient(total_loss, self.local_model.trainable_weights)
# Push local gradients to global model
self.opt.apply_gradients(zip(grads,
self.global_model.trainable_weights))
# Update local model with new weights
self.local_model.set_weights(self.global_model.get_weights())
mem.clear()
time_count = 0
if done: # done and print information
Worker.global_moving_average_reward = \
record(Worker.global_episode, ep_reward, self.worker_idx,
Worker.global_moving_average_reward, self.result_queue,
self.ep_loss, ep_steps)
# We must use a lock to save our model and to print to prevent data races.
if ep_reward > Worker.best_score:
with Worker.save_lock:
print("Saving best model to {}, "
"episode score: {}".format(self.save_dir, ep_reward))
self.global_model.save_weights(
os.path.join(self.save_dir,
'model_{}.h5'.format(self.game_name))
)
Worker.best_score = ep_reward
Worker.global_episode += 1
ep_steps += 1
time_count += 1
current_state = new_state
total_step += 1
self.result_queue.put(None)
def compute_loss(self,
done,
new_state,
memory,
gamma=0.99):
if done:
reward_sum = 0. # terminal
else:
reward_sum = self.local_model(
tf.convert_to_tensor(new_state[None, :],
dtype=tf.float32))[-1].numpy()[0]
# Get discounted rewards
discounted_rewards = []
for reward in memory.rewards[::-1]: # reverse buffer r
reward_sum = reward + gamma * reward_sum
discounted_rewards.append(reward_sum)
discounted_rewards.reverse()
logits, values = self.local_model(
tf.convert_to_tensor(np.vstack(memory.states),
dtype=tf.float32))
# Get our advantages
advantage = tf.convert_to_tensor(np.array(discounted_rewards)[:, None],
dtype=tf.float32) - values
# Value loss
value_loss = advantage ** 2
# Calculate our policy loss
policy = tf.nn.softmax(logits)
entropy = tf.nn.softmax_cross_entropy_with_logits(labels=policy, logits=logits)
policy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=memory.actions,
logits=logits)
policy_loss *= tf.stop_gradient(advantage)
policy_loss -= 0.01 * entropy
total_loss = tf.reduce_mean((0.5 * value_loss + policy_loss))
return total_loss
if __name__ == '__main__':
print(args)
master = MasterAgent()
if args.train:
master.train()
else:
master.play()
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