def test_kick_and_quit(self, env):
env.reset()
kick = env.actions.index(nethack.Command.KICK)
obs, reward, done, _ = env.step(kick)
assert b"In what direction? " in bytes(obs["message"])
env.step(nethack.MiscAction.MORE)
# Hack to quit.
env.nethack.step(nethack.M("q"))
obs, reward, done, _ = env.step(env.actions.index(ord("y")))
assert done
assert reward == 0.0
def test_ttyrec_every(self):
path = pathlib.Path(".")
env = gym.make("NetHackChallenge-v0", save_ttyrec_every=2, savedir=str(path))
pid = os.getpid()
for episode in range(10):
env.reset()
for c in [ord(" "), ord(" "), ord("<"), ord("y")]:
_, _, done, *_ = env.step(env.actions.index(c))
assert done
if episode % 2 != 0:
continue
contents = set(str(p) for p in path.iterdir())
# `contents` includes xlogfile and ttyrecs.
assert len(contents) - 1 == episode // 2 + 1
assert (
"nle.%i.%i.ttyrec%i.bz2" % (pid, episode, nethack.TTYREC_VERSION)
in contents
)
assert "nle.%i.xlogfile" % pid in contents
with open("nle.%i.xlogfile" % pid, "r") as f:
entries = f.readlines()
assert len(entries) == 10
def test_render_ansi(self, env_name, rollout_len):
env = gym.make(env_name)
env.reset()
for _ in range(rollout_len):
action = env.action_space.sample()
_, _, done, _ = env.step(action)
if done:
env.reset()
output = env.render(mode="ansi")
assert isinstance(output, str)
assert len(output.replace("\n", "")) == np.prod(nle.env.DUNGEON_SHAPE)
def main(args):
env = make_venv(args)
env.reset()
start_time = time.time()
for i in range(args.num_steps):
env.step([np.random.randint(8)] * args.num_env)
if (i - 1) % 200 == 0:
env.reset()
total_time_multi = time.time() - start_time
print(
"Took {:.2f}s with subproc={} on {} steps on {} envs - {:.2f} FPS".format(
total_time_multi,
args.subproc,
args.num_steps,
args.num_env,
args.num_steps / total_time_multi,
)
)
def test_final_reward(self, env):
obs = env.reset()
for _ in range(100):
obs, reward, done, info = env.step(env.action_space.sample())
if done:
break
if done:
assert reward == 0.0
return
# Hopefully, we got some positive reward by now.
# Get out of any menu / yn_function.
env.step(env.actions.index(ord("\r")))
# Hack to quit.
env.nethack.step(nethack.M("q"))
_, reward, done, _ = env.step(env.actions.index(ord("y")))
assert done
assert reward == 0.0
def rollout_env(env, max_rollout_len):
"""Produces a rollout and asserts step outputs.
Does *not* assume that the environment has already been reset.
"""
obs = env.reset()
assert env.observation_space.contains(obs)
step = 0
while True:
a = env.action_space.sample()
obs, reward, done, info = env.step(a)
assert env.observation_space.contains(obs)
assert isinstance(reward, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
if done or step >= max_rollout_len:
break
env.close()
def rollout_env(env, max_rollout_len):
"""Produces a rollout and asserts step outputs.
Returns final reward. Does not assume that the environment has already been
reset.
"""
obs = env.reset()
assert env.observation_space.contains(obs)
for _ in range(max_rollout_len):
a = env.action_space.sample()
obs, reward, done, info = env.step(a)
assert env.observation_space.contains(obs)
assert isinstance(reward, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
if done:
break
env.close()
return reward
def test_no_reset(self, env):
with pytest.raises(RuntimeError, match="step called without reset()"):
env.step(0)
