def main():
args = parse_args()
print(args)
maxlen_obs = 150
maxlen_look = 150
maxlen_inv = 50
max_len_action = 12
sp = spm.SentencePieceProcessor()
sp.Load('../spm_models/unigram_8k.model')
rom_path = args.rom_path + utils.game_file(args.game_name)
policy = Policy(args)
# policy.model.load_weights('weights/%s_%s_round%s.5000.h5' % (args.game_name, args.uct_type, args.round))
policy.load_weights(
'gcp/weights/%s/round_%d/%s_weight_policy_best_seed%d.pickle' %
(args.game_name, args.round, args.uct_type, args.seed))
# 63 / 100
env = JerichoEnv(rom_path, 1, args.env_step_limit)
env.create()
scores = []
for seed in range(5):
env = JerichoEnv(rom_path, seed, args.env_step_limit)
env.create()
obs, info = env.reset()
cum_reward = 0
step = 0
prev_action = '<s>'
# livingroom_steps = ["S", "E"]
#
# for action in livingroom_steps:
# obs, reward, done, info = env.step(action)
# prev_action = action
for _ in range(args.max_episode_len):
print('#################################################')
print('STEP: %s' % step)
print()
print(info['look'])
print()
print(info['inv'])
print()
obs, look, inv, prev_action, score = utils.state_representation(
obs, info['look'], info['inv'], prev_action, info['score'],
maxlen_obs, maxlen_look, maxlen_inv, max_len_action)
probs = policy.calculate_probs(obs, look, inv, prev_action, score,
info['valid'])
print(info['valid'])
print(probs)
idx = np.argmax(probs)
# idx = int(np.random.choice([i for i in range(probs.shape[0])], 1, p=probs[:,0]))
action = info['valid'][idx]
obs, reward, done, info = env.step(action)
cum_reward += reward
step += 1
print('ACTION: %s' % action)
print()
print('Reward: %s, Score: %s' % (reward, info['score']))
print()
print(obs + info['look'] + info['inv'])
print()
prev_action = action
scores.append(info['score'])
print(scores)
print('AVERAGE SCORE: %s' % np.mean(scores))
f = open('outputs/eval_result_%s_%s.txt' % (args.game_name, args.uct_type),
'a')
f.write("- Round %d (learning) : num_eval=%d, mean_ep_return=%.3f, std_ep_return=%.3f\n" % \
(args.round, len(scores), np.mean(scores), np.std(scores)))
f.close()
