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# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence | ||
import time | ||
import joblib | ||
import numpy as np | ||
import tensorflow as tf | ||
import os | ||
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def set_global_seeds(i): | ||
tf.set_random_seed(i) | ||
np.random.seed(i) | ||
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def cat_entropy(logits): | ||
a0 = logits - tf.reduce_max(logits, 1, keepdims=True) | ||
ea0 = tf.exp(a0) | ||
z0 = tf.reduce_sum(ea0, 1, keepdims=True) | ||
p0 = ea0 / z0 | ||
return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1) | ||
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def find_trainable_variables(key): | ||
with tf.variable_scope(key): | ||
return tf.trainable_variables() | ||
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def discount_with_dones(rewards, dones, gamma): | ||
discounted = [] | ||
r = 0 | ||
for reward, done in zip(rewards[::-1], dones[::-1]): | ||
r = reward + gamma * r * (1. - done) # fixed off by one bug | ||
discounted.append(r) | ||
return discounted[::-1] | ||
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class Agent: | ||
def __init__(self, Network, ob_space, ac_space, nenvs, nsteps, nstack, | ||
ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4, | ||
alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6)): | ||
config = tf.ConfigProto(intra_op_parallelism_threads=nenvs, | ||
inter_op_parallelism_threads=nenvs) | ||
config.gpu_options.allow_growth = True | ||
sess = tf.Session(config=config) | ||
nbatch = nenvs * nsteps | ||
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A = tf.placeholder(tf.int32, [nbatch]) | ||
ADV = tf.placeholder(tf.float32, [nbatch]) | ||
R = tf.placeholder(tf.float32, [nbatch]) | ||
LR = tf.placeholder(tf.float32, []) | ||
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step_model = Network(sess, ob_space, ac_space, nenvs, 1, nstack, reuse=False) | ||
train_model = Network(sess, ob_space, ac_space, nenvs, nsteps, nstack, reuse=True) | ||
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neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A) | ||
pg_loss = tf.reduce_mean(ADV * neglogpac) | ||
vf_loss = tf.reduce_mean(tf.squared_difference(tf.squeeze(train_model.vf), R) / 2.0) | ||
entropy = tf.reduce_mean(cat_entropy(train_model.pi)) | ||
loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef | ||
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params = find_trainable_variables("model") | ||
grads = tf.gradients(loss, params) | ||
if max_grad_norm is not None: | ||
grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) | ||
grads_and_params = list(zip(grads, params)) | ||
trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon) | ||
_train = trainer.apply_gradients(grads_and_params) | ||
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def train(states, rewards, actions, values): | ||
advs = rewards - values | ||
feed_dict = {train_model.X: states, A: actions, ADV: advs, R: rewards, LR: lr} | ||
policy_loss, value_loss, policy_entropy, _ = sess.run( | ||
[pg_loss, vf_loss, entropy, _train], | ||
feed_dict | ||
) | ||
return policy_loss, value_loss, policy_entropy | ||
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def save(save_path): | ||
ps = sess.run(params) | ||
joblib.dump(ps, save_path) | ||
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def load(load_path): | ||
loaded_params = joblib.load(load_path) | ||
restores = [] | ||
for p, loaded_p in zip(params, loaded_params): | ||
restores.append(p.assign(loaded_p)) | ||
ps = sess.run(restores) | ||
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self.train = train | ||
self.train_model = train_model | ||
self.step_model = step_model | ||
self.step = step_model.step | ||
self.value = step_model.value | ||
self.save = save | ||
self.load = load | ||
tf.global_variables_initializer().run(session=sess) | ||
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class Runner: | ||
def __init__(self, env, agent, nsteps=5, nstack=4, gamma=0.99): | ||
self.env = env | ||
self.agent = agent | ||
nh, nw, nc = env.observation_space.shape | ||
nenv = env.num_envs | ||
self.batch_ob_shape = (nenv * nsteps, nh, nw, nc * nstack) | ||
self.state = np.zeros((nenv, nh, nw, nc * nstack), dtype=np.uint8) | ||
self.nc = nc | ||
obs = env.reset() | ||
self.update_state(obs) | ||
self.gamma = gamma | ||
self.nsteps = nsteps | ||
self.dones = [False for _ in range(nenv)] | ||
self.total_rewards = [] # store all workers' total rewards | ||
self.real_total_rewards = [] | ||
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def update_state(self, obs): | ||
# Do frame-stacking here instead of the FrameStack wrapper to reduce IPC overhead | ||
self.state = np.roll(self.state, shift=-self.nc, axis=3) | ||
self.state[:, :, :, -self.nc:] = obs | ||
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def run(self): | ||
mb_states, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], [] | ||
for n in range(self.nsteps): | ||
actions, values = self.agent.step(self.state) | ||
mb_states.append(np.copy(self.state)) | ||
mb_actions.append(actions) | ||
mb_values.append(values) | ||
mb_dones.append(self.dones) | ||
obs, rewards, dones, infos = self.env.step(actions) | ||
for done, info in zip(dones, infos): | ||
if done: | ||
self.total_rewards.append(info['reward']) | ||
if info['total_reward'] != -1: | ||
self.real_total_rewards.append(info['total_reward']) | ||
self.dones = dones | ||
for n, done in enumerate(dones): | ||
if done: | ||
self.state[n] = self.state[n] * 0 | ||
self.update_state(obs) | ||
mb_rewards.append(rewards) | ||
mb_dones.append(self.dones) | ||
# batch of steps to batch of rollouts | ||
mb_states = np.asarray(mb_states, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape) | ||
mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0) | ||
mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0) | ||
mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0) | ||
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0) | ||
mb_dones = mb_dones[:, 1:] | ||
last_values = self.agent.value(self.state).tolist() | ||
# discount/bootstrap off value fn | ||
for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)): | ||
rewards = rewards.tolist() | ||
dones = dones.tolist() | ||
if dones[-1] == 0: | ||
rewards = discount_with_dones(rewards + [value], dones + [0], self.gamma)[:-1] | ||
else: | ||
rewards = discount_with_dones(rewards, dones, self.gamma) | ||
mb_rewards[n] = rewards | ||
mb_rewards = mb_rewards.flatten() | ||
mb_actions = mb_actions.flatten() | ||
mb_values = mb_values.flatten() | ||
return mb_states, mb_rewards, mb_actions, mb_values | ||
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def learn(network, env, seed, new_session=True, nsteps=5, nstack=4, total_timesteps=int(80e6), | ||
vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, | ||
epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=1000): | ||
tf.reset_default_graph() | ||
set_global_seeds(seed) | ||
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nenvs = env.num_envs | ||
env_id = env.env_id | ||
save_name = os.path.join('models', env_id + '.save') | ||
ob_space = env.observation_space | ||
ac_space = env.action_space | ||
agent = Agent(Network=network, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, | ||
nsteps=nsteps, nstack=nstack, | ||
ent_coef=ent_coef, vf_coef=vf_coef, | ||
max_grad_norm=max_grad_norm, | ||
lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps) | ||
if os.path.exists(save_name): | ||
agent.load(save_name) | ||
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runner = Runner(env, agent, nsteps=nsteps, nstack=nstack, gamma=gamma) | ||
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nbatch = nenvs * nsteps | ||
tstart = time.time() | ||
for update in range(1, total_timesteps // nbatch + 1): | ||
states, rewards, actions, values = runner.run() | ||
policy_loss, value_loss, policy_entropy = agent.train( | ||
states, rewards, actions, values) | ||
nseconds = time.time() - tstart | ||
fps = int((update * nbatch) / nseconds) | ||
if update % log_interval == 0 or update == 1: | ||
print(' - - - - - - - ') | ||
print("nupdates", update) | ||
print("total_timesteps", update * nbatch) | ||
print("fps", fps) | ||
print("policy_entropy", float(policy_entropy)) | ||
print("value_loss", float(value_loss)) | ||
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# total reward | ||
r = runner.total_rewards[-100:] # get last 100 | ||
tr = runner.real_total_rewards[-100:] | ||
if len(r) == 100: | ||
print("avg reward (last 100):", np.mean(r)) | ||
if len(tr) == 100: | ||
print("avg total reward (last 100):", np.mean(tr)) | ||
print("max (last 100):", np.max(tr)) | ||
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agent.save(save_name) | ||
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env.close() | ||
agent.save(save_name) |
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