import tensorflow as tf
from env import Environment
from policy_agent import PolicyAgent
from model import Model
from args import parse_args
_, CONFIG, args = parse_args('check-js')
env = Environment()
env.add_opponent(PolicyAgent(env, policy='downsize'))
with tf.Session() as sess:
model = Model(CONFIG, env, sess, None, name='haggle')
saver = tf.train.Saver(max_to_keep=10000, name='test')
saver.restore(sess, args.source)
print(sess.run(model.action_probs, feed_dict={
model.input: [ [
# available actions
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 3, 4, 0, 2, 1, 1
] ],
model.rnn_state: [ model.zero_state ],
model.is_first_round: [ True ],
}))
import numpy as np from env import Environment from policy_agent import PolicyAgent env = Environment() estimator = PolicyAgent(env, policy='estimator') env.add_opponent(estimator) print(env.bench(PolicyAgent(env, policy='downsize'), times=3000)) env.reset() print(env._make_state())
bench_env = {
'half_or_all': Environment(),
'downsize': Environment(),
'estimator': Environment()
}
bench_env['half_or_all'].add_opponent( \
PolicyAgent(bench_env['half_or_all'], policy='half_or_all'))
bench_env['downsize'].add_opponent( \
PolicyAgent(bench_env['downsize'], policy='downsize'))
bench_env['estimator'].add_opponent( \
PolicyAgent(bench_env['estimator'], policy='estimator'))
for i in range(CONCURRENCY):
env = Environment()
env.add_opponent(PolicyAgent(env, policy='half_or_all'))
env.add_opponent(PolicyAgent(env, policy='downsize'))
env.add_opponent(PolicyAgent(env, policy='estimator'))
env_list.append(env)
writer = tf.summary.FileWriter(LOG_DIR)
with tf.Session() as sess:
print('Initializing model')
model = Model(CONFIG, env_list[0], sess, writer, name='haggle')
writer.add_graph(tf.get_default_graph())
saver = tf.train.Saver(max_to_keep=100, name=RUN_NAME)
for i in range(NUM_ANTAGONISTS):
print('Initializing antagonist {}'.format(i))
