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bc_pretrain.py
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bc_pretrain.py
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import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]='0' # '1', '2', '3', '0,1'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import csv
import time
import math
import random
import argparse
import numpy as np
# import gym
import cv2
import tensorflow as tf
from env.navigate import NaviEnv
from models.gail import Actor
# from models.Global_Encoder import Encoder
from models.VAE_Network import VAE_Encoder
from utils.memory import HorizonMemory, ReplayMemory
from utils.util import *
from utils.config import *
class BC:
def __init__(self, global_norm, actor_lr, actor_units,
epochs, batch_size, data_dir, demo_list):
# build network
self.actor = Actor(lr=actor_lr, hidden_units=actor_units)
self.encoder = VAE_Encoder(latent_num=64)
self.opt = tf.keras.optimizers.Adam(learning_rate=actor_lr)
# set hyperparameters
self.epochs = epochs
self.batch_size = batch_size
self.grad_global_norm = global_norm
# build expert demonstration Pipeline
self.data_dir = data_dir
self.demo_list = os.listdir(data_dir)
self.demo_group_num = 1000
self.demo_rotate = 20
assert len(demo_list) >= self.demo_group_num
self.set_demo()
# ready
self.dummy_forward()
self.vars = self.actor.trainable_variables + self.encoder.trainable_variables
def dummy_forward(self):
# connect networks
dummy_state = np.zeros([1] + STATE_SHAPE, dtype=np.float32)
self.encoder(dummy_state)
self.actor(self.encoder, dummy_state)
def set_demo(self):
self.demo_list = os.listdir(data_dir)
selected_demos = random.sample(self.demo_list, self.demo_group_num)
expert_states = []
expert_actions = []
for demo_name in selected_demos:
demo = np.load(self.data_dir + demo_name)
states = demo['state']
actions = demo['action']
expert_states.append(states)
expert_actions.append(actions)
self.expert_states = np.concatenate(expert_states, axis=0)
self.expert_actions = np.concatenate(expert_actions, axis=0)
del demo
def get_demonstration(self, sample_num):
index = np.arange(len(self.expert_states))
try:
assert len(self.expert_states) >= sample_num
except Exception:
self.set_demo()
np.random.shuffle(index)
index = index[:sample_num]
return self.expert_states[index], self.expert_actions[index]
def update(self):
# load expert demonstration
s_e, a_e = self.get_demonstration(self.batch_size * self.epochs)
batch_num = len(s_e) // self.batch_size
index = np.arange(len(s_e))
np.random.shuffle(index)
loss = 0
for i in range(batch_num):
idx = index[i*self.batch_size : (i+1)*self.batch_size]
state = s_e[idx]
action = a_e[idx]
# update actor
with tf.GradientTape() as tape:
pred_action = self.actor(self.encoder, state) # (N, A)
# CE
actor_loss = -tf.reduce_mean(tf.reduce_sum(action * tf.math.log(pred_action + 1e-8), axis=1))
grads = tape.gradient(actor_loss, self.vars)
if self.grad_global_norm > 0:
grads, _ = tf.clip_by_global_norm(grads, self.grad_global_norm)
self.opt.apply_gradients(zip(grads, self.vars))
loss += actor_loss.numpy()
return loss / batch_num
def save_model(self, dir, tag=''):
self.actor.save_weights(dir + tag + 'actor.h5')
self.encoder.save_weights(dir + tag + 'encoder.h5')
def load_model(self, dir, tag=''):
if os.path.exists(dir + tag + 'actor.h5'):
self.actor.load_weights(dir + tag + 'actor.h5')
print('Actor loaded... %s%sactor.h5' % (dir, tag))
if os.path.exists(dir + tag + 'encoder.h5'):
self.encoder.load_weights(dir + tag + 'encoder.h5')
print('encoder loaded... %s%sencoder.h5' % (dir, tag))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--load_model', action='store_true')
parser.add_argument('--render', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('--global_norm',type=float, default=0.0)
parser.add_argument('--actor_lr', type=float, default=1e-4)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--save_rate', type=int, default=20)
parser.add_argument('--env', type=str, default='Navi-v1')
parser.add_argument('--log_name', type=str, default='bc')
parser.add_argument('--actor_units', type=int, nargs='*', default=[256, 128])
args = parser.parse_args()
env_name = args.env
data_dir = 'data/%s/' % env_name
model_dir = 'weights/%s/' % args.log_name
demo_list = os.listdir(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
agent = BC(
global_norm=args.global_norm,
actor_lr=args.actor_lr,
actor_units=args.actor_units,
epochs=args.epochs,
batch_size=args.batch_size,
data_dir=data_dir,
demo_list=demo_list
)
if args.load_model:
agent.load_model(model_dir)
# Environment Interaction Iteration
epoch = 0
stats = []
while True:
# reset demo
if epoch % agent.demo_rotate == 0:
agent.set_demo()
loss = agent.update()
if args.verbose:
print('E:%d... loss: %.4f\t\t\t' % (epoch, loss), end='\r')
# done
stats.append([loss])
if epoch % args.save_rate == 0:
m_loss = np.mean(stats, axis=0)
agent.save_model(model_dir)
with open('%s.csv' % args.log_name, 'a', newline='') as f:
wrt = csv.writer(f)
for row in stats:
wrt.writerow(row)
stats.clear()
epoch += 1