def create_env(cls, flags, featurizer=None):
f = featurizer or X.Concat([X.Text(), X.ValidMoves()])
print('loading env')
start_time = time.time()
env = gym.make(flags.env, room_shape=(flags.height, flags.width), partially_observable=flags.partial_observability, max_placement=flags.max_placement, featurizer=f, shuffle_wiki=flags.shuffle_wiki, time_penalty=flags.time_penalty)
print('loaded env in {} seconds'.format(time.time() - start_time))
return env
def test(flags, num_eps: int = 1000):
from rtfm import featurizer as X
gym_env = Net.create_env(flags)
if flags.mode == 'test_render':
gym_env.featurizer = X.Concat([gym_env.featurizer, X.Terminal()])
env = environment.Environment(gym_env)
if not flags.random_agent:
model = Net.make(flags, gym_env)
model.eval()
if flags.xpid is None:
checkpointpath = './results_latest/model.tar'
else:
checkpointpath = os.path.expandvars(
os.path.expanduser('%s/%s/%s' % (flags.savedir, flags.xpid,
'model.tar')))
checkpoint = torch.load(checkpointpath, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
observation = env.initial()
returns = []
won = []
entropy = []
ep_len = []
while len(won) < num_eps:
done = False
steps = 0
while not done:
if flags.random_agent:
action = torch.zeros(1, 1, dtype=torch.int32)
action[0][0] = random.randint(0, gym_env.action_space.n - 1)
observation = env.step(action)
else:
agent_outputs = model(observation)
observation = env.step(agent_outputs['action'])
policy = F.softmax(agent_outputs['policy_logits'], dim=-1)
log_policy = F.log_softmax(agent_outputs['policy_logits'], dim=-1)
e = -torch.sum(policy * log_policy, dim=-1)
entropy.append(e.mean(0).item())
steps += 1
done = observation['done'].item()
if observation['done'].item():
returns.append(observation['episode_return'].item())
won.append(observation['reward'][0][0].item() > 0.5)
ep_len.append(steps)
# logging.info('Episode ended after %d steps. Return: %.1f',
# observation['episode_step'].item(),
# observation['episode_return'].item())
if flags.mode == 'test_render':
sleep_seconds = os.environ.get('DELAY', '0.3')
time.sleep(float(sleep_seconds))
if observation['done'].item():
print('Done: {}'.format('You won!!' if won[-1] else 'You lost!!'))
print('Episode steps: {}'.format(observation['episode_step']))
print('Episode return: {}'.format(observation['episode_return']))
done_seconds = os.environ.get('DONE', None)
if done_seconds is None:
print('Press Enter to continue')
input()
else:
time.sleep(float(done_seconds))
env.close()
logging.info('Average returns over %i episodes: %.2f. Win rate: %.2f. Entropy: %.2f. Len: %.2f', num_eps, sum(returns)/len(returns), sum(won)/len(returns), sum(entropy)/max(1, len(entropy)), sum(ep_len)/len(ep_len))
def create_env(cls, flags, featurizer=None):
return super().create_env(flags, featurizer=featurizer or X.Concat([X.Text(), X.ValidMoves(), X.RelativePosition()]))
from rtfm.dynamics.monster import Player
from rtfm import featurizer as F
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--env', help='environment to use', default='rock_paper_scissors')
parser.add_argument('--height', type=int, help='room size', default=12)
parser.add_argument('--width', type=int, help='room size', default=22)
parser.add_argument('--seed', type=int, help='seed', default=42)
parser.add_argument('-p', '--partially_observable', action='store_true', help='only show partial observability')
parser.add_argument('-c', '--control', action='store_true', help='assume direct control')
parser.add_argument('-w', '--shuffle_wiki', action='store_true', help='shuffle facts in the wiki')
args = parser.parse_args()
featurizer = F.Concat([F.Progress(), F.ValidMoves(), F.Terminal()])
env = gym.make('rtfm:{}-v0'.format(args.env), featurizer=featurizer, partially_observable=args.partially_observable, room_shape=(args.height, args.width), shuffle_wiki=args.shuffle_wiki)
random.seed(args.seed)
score = total = 0
while True:
env.reset()
feat = reward = finished = won = None
while not finished:
if args.control:
ch = None
while ch not in Player.keymap:
print('Current score {} out of {}'.format(score, total))
print('Enter your command. x to quit.')
ch = getch.getch()