def __init__(
self,
env,
n_bins_obs=10,
memory_size=100,
state_preprocess_fn=None,
state_preprocess_kwargs=None,
):
Wrapper.__init__(self, env)
if state_preprocess_fn is None:
assert isinstance(env.observation_space, spaces.Box)
assert isinstance(env.action_space, spaces.Discrete)
self.state_preprocess_fn = state_preprocess_fn or identity
self.state_preprocess_kwargs = state_preprocess_kwargs or {}
self.memory = TrajectoryMemory(memory_size)
self.total_visit_counter = DiscreteCounter(self.env.observation_space,
self.env.action_space,
n_bins_obs=n_bins_obs)
self.episode_visit_counter = DiscreteCounter(
self.env.observation_space,
self.env.action_space,
n_bins_obs=n_bins_obs)
self.current_state = None
self.curret_step = 0
def __init__(self, env):
Wrapper.__init__(self, env, wrap_spaces=True)
obs_space = self.env.observation_space
assert isinstance(obs_space, Discrete)
self.observation_space = Box(
low=0.0, high=1.0, shape=(obs_space.n,), dtype=np.uint32
)
def __init__(self, env, horizon):
"""
Parameters
----------
horizon: int
"""
Wrapper.__init__(self, env)
self.horizon = horizon
assert self.horizon >= 1
self.current_step = 0
def test_double_wrapper_copy_reseeding(ModelClass):
seeding.set_global_seed(123)
env = Wrapper(Wrapper(ModelClass()))
c_env = deepcopy(env)
c_env.reseed()
if deepcopy(env).is_online():
traj1 = get_env_trajectory(env, 500)
traj2 = get_env_trajectory(c_env, 500)
assert not compare_trajectories(traj1, traj2)
def test_wrapper():
env = GridWorld()
wrapped = Wrapper(env)
assert isinstance(wrapped, Model)
assert wrapped.is_online()
assert wrapped.is_generative()
# calling some functions
wrapped.reset()
wrapped.step(wrapped.action_space.sample())
wrapped.sample(wrapped.observation_space.sample(),
wrapped.action_space.sample())
def test_gym_wrapper():
gym_env = gym.make("Acrobot-v1")
wrapped = Wrapper(gym_env)
assert isinstance(wrapped, Model)
assert wrapped.is_online()
assert not wrapped.is_generative()
wrapped.reseed()
# calling some gym functions
wrapped.close()
wrapped.seed()
def test_wrapper_copy_reseeding(ModelClass):
env = Wrapper(ModelClass())
seeder = Seeder(123)
env.reseed(seeder)
c_env = deepcopy(env)
c_env.reseed()
if deepcopy(env).is_online():
traj1 = get_env_trajectory(env, 500)
traj2 = get_env_trajectory(c_env, 500)
assert not compare_trajectories(traj1, traj2)
def test_gym_copy_reseeding_2():
seeding.set_global_seed(123)
if _GYM_INSTALLED:
gym_env = gym.make('Acrobot-v1')
# nested wrapping
env = RescaleRewardWrapper(Wrapper(Wrapper(gym_env)), (0, 1))
c_env = deepcopy(env)
c_env.reseed()
if deepcopy(env).is_online():
traj1 = get_env_trajectory(env, 500)
traj2 = get_env_trajectory(c_env, 500)
assert not compare_trajectories(traj1, traj2)
def test_gym_copy_reseeding():
seeder = Seeder(123)
if _GYM_INSTALLED:
gym_env = gym.make("Acrobot-v1")
env = Wrapper(gym_env)
env.reseed(seeder)
c_env = deepcopy(env)
c_env.reseed()
if deepcopy(env).is_online():
traj1 = get_env_trajectory(env, 500)
traj2 = get_env_trajectory(c_env, 500)
assert not compare_trajectories(traj1, traj2)
def test_wrapper_seeding(ModelClass):
seeding.set_global_seed(123)
env1 = Wrapper(ModelClass())
seeding.set_global_seed(456)
env2 = Wrapper(ModelClass())
seeding.set_global_seed(123)
env3 = Wrapper(ModelClass())
if deepcopy(env1).is_online():
traj1 = get_env_trajectory(env1, 500)
traj2 = get_env_trajectory(env2, 500)
traj3 = get_env_trajectory(env3, 500)
assert not compare_trajectories(traj1, traj2)
assert compare_trajectories(traj1, traj3)
def __init__(self,
env,
uncertainty_estimator_fn,
uncertainty_estimator_kwargs=None,
bonus_scale_factor=1.0,
bonus_max=np.inf):
Wrapper.__init__(self, env)
self.bonus_scale_factor = bonus_scale_factor
self.bonus_max = bonus_max
uncertainty_estimator_kwargs = uncertainty_estimator_kwargs or {}
uncertainty_estimator_fn = load(uncertainty_estimator_fn) if isinstance(uncertainty_estimator_fn, str) else \
uncertainty_estimator_fn
self.uncertainty_estimator = uncertainty_estimator_fn(
env.observation_space, env.action_space,
**uncertainty_estimator_kwargs)
self.previous_obs = None
def __init__(self, env):
Wrapper.__init__(self, env)
def __init__(self, env, reward_range):
Wrapper.__init__(self, env)
self.reward_range = reward_range
assert reward_range[0] < reward_range[1]
assert reward_range[0] > -np.inf and reward_range[1] < np.inf
def test_render2d_interface_wrapped(ModelClass):
env = Wrapper(ModelClass())
if isinstance(env.env, RenderInterface2D):
env.enable_rendering()
if env.is_online():
for _ in range(2):
state = env.reset()
for _ in range(5):
assert env.observation_space.contains(state)
action = env.action_space.sample()
next_s, _, _, _ = env.step(action)
state = next_s
env.render(loop=False)
env.save_video("test_video.mp4")
env.clear_render_buffer()
try:
os.remove("test_video.mp4")
except Exception:
pass
def test_wrapper_seeding(ModelClass):
env1 = Wrapper(ModelClass())
seeder = Seeder(123)
env1.reseed(seeder)
env2 = Wrapper(ModelClass())
seeder = Seeder(456)
env2.reseed(seeder)
env3 = Wrapper(ModelClass())
seeder = Seeder(123)
env3.reseed(seeder)
if deepcopy(env1).is_online():
traj1 = get_env_trajectory(env1, 500)
traj2 = get_env_trajectory(env2, 500)
traj3 = get_env_trajectory(env3, 500)
assert not compare_trajectories(traj1, traj2)
assert compare_trajectories(traj1, traj3)
