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())
simple_value=no_consensus_score)
writer.add_summary(summary, model.writer_step)
for env in env_list:
env.no_consensus_score = no_consensus_score
model.explore(env_list, game_count=1024, game_off=game_off)
game_off += 1024
if EPOCH % SAVE_EVERY == 0:
saver.save(sess, os.path.join(SAVE_DIR, '{:08d}'.format(EPOCH)))
if EPOCH % BENCH_EVERY == 0:
print('Running benchmark...')
for tag, env in bench_env.items():
bench = env.bench(model)
# TODO(indutny): move logging to model?
summary = tf.Summary()
for key, value in bench.items():
summary.value.add(tag='bench_{}/{}'.format(tag, key),
simple_value=value)
writer.add_summary(summary, model.writer_step)
if EPOCH < ANTAGONIST_EPOCH:
continue
if EPOCH % ANTAGONIST_UPDATE_EVERY == 0:
print('Adding new antagonist to the pool')
weights = model.save_weights(sess)
ANTAGONIST_WEIGHTS.append({'epoch': EPOCH, 'weights': weights})
