79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
#!/usr/bin/env python
|
|
from __future__ import absolute_import
|
|
|
|
import tensorflow as tf
|
|
import gym
|
|
import numpy as np
|
|
import time
|
|
|
|
# our lib imports here! It's ok to append path in examples
|
|
import sys
|
|
sys.path.append('..')
|
|
from tianshou.core import losses
|
|
from tianshou.data.batch import Batch
|
|
import tianshou.data.advantage_estimation as advantage_estimation
|
|
import tianshou.core.policy.dqn as policy # TODO: fix imports as zhusuan so that only need to import to policy
|
|
import tianshou.core.value_function.action_value as value_function
|
|
|
|
|
|
if __name__ == '__main__':
|
|
env = gym.make('CartPole-v0')
|
|
observation_dim = env.observation_space.shape
|
|
action_dim = env.action_space.n
|
|
|
|
clip_param = 0.2
|
|
num_batches = 10
|
|
batch_size = 512
|
|
|
|
seed = 0
|
|
np.random.seed(seed)
|
|
tf.set_random_seed(seed)
|
|
|
|
### 1. build network with pure tf
|
|
observation_ph = tf.placeholder(tf.float32, shape=(None,) + observation_dim)
|
|
|
|
def my_network():
|
|
net = tf.layers.dense(observation_ph, 32, activation=tf.nn.tanh)
|
|
net = tf.layers.dense(net, 32, activation=tf.nn.tanh)
|
|
|
|
action_values = tf.layers.dense(net, action_dim, activation=None)
|
|
|
|
return None, action_values # no policy head
|
|
|
|
### 2. build policy, loss, optimizer
|
|
dqn = value_function.DQN(my_network, observation_placeholder=observation_ph, weight_update=100)
|
|
pi = policy.DQN(dqn)
|
|
|
|
dqn_loss = losses.qlearning(dqn)
|
|
|
|
total_loss = dqn_loss
|
|
optimizer = tf.train.AdamOptimizer(1e-4)
|
|
train_op = optimizer.minimize(total_loss, var_list=dqn.trainable_variables)
|
|
|
|
### 3. define data collection
|
|
data_collector = Batch(env, pi, [advantage_estimation.nstep_q_return(1, dqn)], [dqn])
|
|
|
|
### 4. start training
|
|
config = tf.ConfigProto()
|
|
config.gpu_options.allow_growth = True
|
|
with tf.Session(config=config) as sess:
|
|
sess.run(tf.global_variables_initializer())
|
|
|
|
# assign actor to pi_old
|
|
pi.sync_weights() # TODO: automate this for policies with target network
|
|
|
|
start_time = time.time()
|
|
for i in range(100):
|
|
# collect data
|
|
data_collector.collect(num_episodes=50)
|
|
|
|
# print current return
|
|
print('Epoch {}:'.format(i))
|
|
data_collector.statistics()
|
|
|
|
# update network
|
|
for _ in range(num_batches):
|
|
feed_dict = data_collector.next_batch(batch_size)
|
|
sess.run(train_op, feed_dict=feed_dict)
|
|
|
|
print('Elapsed time: {:.1f} min'.format((time.time() - start_time) / 60)) |