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Merge pull request #83 from ALRhub/78_fix_render_mode

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Fix render_mode not working for many fancy envs
This commit is contained in:
Hongyi Zhou 2023-11-22 11:41:13 +01:00 committed by GitHub
commit 216a6f215d
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GPG Key ID: 4AEE18F83AFDEB23
19 changed files with 75 additions and 60 deletions
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15
fancy_gym/black_box/black_box_wrapper.py
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@ -75,7 +75,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.observation_space = self._get_observation_space() self.observation_space = self._get_observation_space()
# rendering # rendering
self.render_kwargs = {} self.do_render = False
self.verbose = verbose self.verbose = verbose
# condition value # condition value
@ -164,7 +164,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
dtype=self.env.observation_space.dtype) dtype=self.env.observation_space.dtype)
infos = dict() infos = dict()
done = False terminated, truncated = False, False
if not traj_is_valid: if not traj_is_valid:
obs, trajectory_return, terminated, truncated, infos = self.env.invalid_traj_callback(action, position, velocity, obs, trajectory_return, terminated, truncated, infos = self.env.invalid_traj_callback(action, position, velocity,
@ -190,8 +190,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
elems[t] = v elems[t] = v
infos[k] = elems infos[k] = elems
if self.render_kwargs: if self.do_render:
self.env.render(**self.render_kwargs) self.env.render()
if terminated or truncated or (self.replanning_schedule(self.env.get_wrapper_attr('current_pos'), self.env.get_wrapper_attr('current_vel'), obs, c_action, t + 1 + self.current_traj_steps) and self.plan_steps < self.max_planning_times): if terminated or truncated or (self.replanning_schedule(self.env.get_wrapper_attr('current_pos'), self.env.get_wrapper_attr('current_vel'), obs, c_action, t + 1 + self.current_traj_steps) and self.plan_steps < self.max_planning_times):
@ -215,10 +216,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
trajectory_return = self.reward_aggregation(rewards[:t + 1]) trajectory_return = self.reward_aggregation(rewards[:t + 1])
return self.observation(obs), trajectory_return, terminated, truncated, infos return self.observation(obs), trajectory_return, terminated, truncated, infos
def render(self, **kwargs): def render(self):
"""Only set render options here, such that they can be used during the rollout. self.do_render = True
This only needs to be called once"""
self.render_kwargs = kwargs
def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \ def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-> Tuple[ObsType, Dict[str, Any]]: -> Tuple[ObsType, Dict[str, Any]]:

4
fancy_gym/envs/classic_control/base_reacher/base_reacher.py
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@ -14,12 +14,14 @@ class BaseReacherEnv(gym.Env):
Base class for all reaching environments. Base class for all reaching environments.
""" """
def __init__(self, n_links: int, random_start: bool = True, allow_self_collision: bool = False): def __init__(self, n_links: int, random_start: bool = True, allow_self_collision: bool = False, render_mode: str = None):
super().__init__() super().__init__()
self.link_lengths = np.ones(n_links) self.link_lengths = np.ones(n_links)
self.n_links = n_links self.n_links = n_links
self._dt = 0.01 self._dt = 0.01
self.render_mode = render_mode
self.random_start = random_start self.random_start = random_start
self.allow_self_collision = allow_self_collision self.allow_self_collision = allow_self_collision

4
fancy_gym/envs/classic_control/base_reacher/base_reacher_direct.py
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@ -10,8 +10,8 @@ class BaseReacherDirectEnv(BaseReacherEnv):
""" """
def __init__(self, n_links: int, random_start: bool = True, def __init__(self, n_links: int, random_start: bool = True,
allow_self_collision: bool = False): allow_self_collision: bool = False, **kwargs):
super().__init__(n_links, random_start, allow_self_collision) super().__init__(n_links, random_start, allow_self_collision, **kwargs)
self.max_vel = 2 * np.pi self.max_vel = 2 * np.pi
action_bound = np.ones((self.n_links,)) * self.max_vel action_bound = np.ones((self.n_links,)) * self.max_vel

4
fancy_gym/envs/classic_control/base_reacher/base_reacher_torque.py
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@ -10,8 +10,8 @@ class BaseReacherTorqueEnv(BaseReacherEnv):
""" """
def __init__(self, n_links: int, random_start: bool = True, def __init__(self, n_links: int, random_start: bool = True,
allow_self_collision: bool = False): allow_self_collision: bool = False, **kwargs):
super().__init__(n_links, random_start, allow_self_collision) super().__init__(n_links, random_start, allow_self_collision, **kwargs)
self.max_torque = 1000 self.max_torque = 1000
action_bound = np.ones((self.n_links,)) * self.max_torque action_bound = np.ones((self.n_links,)) * self.max_torque

10
fancy_gym/envs/classic_control/hole_reacher/hole_reacher.py
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@ -17,9 +17,9 @@ class HoleReacherEnv(BaseReacherDirectEnv):
def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None, def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
hole_width: float = 1., random_start: bool = False, allow_self_collision: bool = False, hole_width: float = 1., random_start: bool = False, allow_self_collision: bool = False,
allow_wall_collision: bool = False, collision_penalty: float = 1000, rew_fct: str = "simple"): allow_wall_collision: bool = False, collision_penalty: float = 1000, rew_fct: str = "simple", **kwargs):
super().__init__(n_links, random_start, allow_self_collision) super().__init__(n_links, random_start, allow_self_collision, **kwargs)
# provided initial parameters # provided initial parameters
self.initial_x = hole_x # x-position of center of hole self.initial_x = hole_x # x-position of center of hole
@ -178,7 +178,7 @@ class HoleReacherEnv(BaseReacherDirectEnv):
return False return False
def render(self, mode='human'): def render(self):
if self.fig is None: if self.fig is None:
# Create base figure once on the beginning. Afterwards only update # Create base figure once on the beginning. Afterwards only update
plt.ion() plt.ion()
@ -197,7 +197,7 @@ class HoleReacherEnv(BaseReacherDirectEnv):
self.fig.gca().set_title( self.fig.gca().set_title(
f"Iteration: {self._steps}, distance: {np.linalg.norm(self.end_effector - self._goal) ** 2}") f"Iteration: {self._steps}, distance: {np.linalg.norm(self.end_effector - self._goal) ** 2}")
if mode == "human": if self.render_mode == "human":
# arm # arm
self.line.set_data(self._joints[:, 0], self._joints[:, 1]) self.line.set_data(self._joints[:, 0], self._joints[:, 1])
@ -205,7 +205,7 @@ class HoleReacherEnv(BaseReacherDirectEnv):
self.fig.canvas.draw() self.fig.canvas.draw()
self.fig.canvas.flush_events() self.fig.canvas.flush_events()
elif mode == "partial": elif self.render_mode == "partial":
if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided: if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided:
# Arm # Arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k',

6
fancy_gym/envs/classic_control/simple_reacher/simple_reacher.py
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@ -17,8 +17,8 @@ class SimpleReacherEnv(BaseReacherTorqueEnv):
""" """
def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True, def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True,
allow_self_collision: bool = False, ): allow_self_collision: bool = False, **kwargs):
super().__init__(n_links, random_start, allow_self_collision) super().__init__(n_links, random_start, allow_self_collision, **kwargs)
# provided initial parameters # provided initial parameters
self.inital_target = target self.inital_target = target
@ -98,7 +98,7 @@ class SimpleReacherEnv(BaseReacherTorqueEnv):
def _check_collisions(self) -> bool: def _check_collisions(self) -> bool:
return self._check_self_collision() return self._check_self_collision()
def render(self, mode='human'): # pragma: no cover def render(self): # pragma: no cover
if self.fig is None: if self.fig is None:
# Create base figure once on the beginning. Afterwards only update # Create base figure once on the beginning. Afterwards only update
plt.ion() plt.ion()

12
fancy_gym/envs/classic_control/viapoint_reacher/viapoint_reacher.py
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@ -13,9 +13,9 @@ from . import MPWrapper
class ViaPointReacherEnv(BaseReacherDirectEnv): class ViaPointReacherEnv(BaseReacherDirectEnv):
def __init__(self, n_links, random_start: bool = False, via_target: Union[None, Iterable] = None, def __init__(self, n_links, random_start: bool = False, via_target: Union[None, Iterable] = None,
target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=1000): target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=1000, **kwargs):
super().__init__(n_links, random_start, allow_self_collision) super().__init__(n_links, random_start, allow_self_collision, **kwargs)
# provided initial parameters # provided initial parameters
self.intitial_target = target # provided target value self.intitial_target = target # provided target value
@ -123,7 +123,7 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
def _check_collisions(self) -> bool: def _check_collisions(self) -> bool:
return self._check_self_collision() return self._check_self_collision()
def render(self, mode='human'): def render(self):
goal_pos = self._goal.T goal_pos = self._goal.T
via_pos = self._via_point.T via_pos = self._via_point.T
@ -146,7 +146,7 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}") self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
if mode == "human": if self.render_mode == "human":
# goal # goal
if self._steps == 1: if self._steps == 1:
self.goal_point_plot.set_data(goal_pos[0], goal_pos[1]) self.goal_point_plot.set_data(goal_pos[0], goal_pos[1])
@ -158,7 +158,7 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
self.fig.canvas.draw() self.fig.canvas.draw()
self.fig.canvas.flush_events() self.fig.canvas.flush_events()
elif mode == "partial": elif self.render_mode == "partial":
if self._steps == 1: if self._steps == 1:
# fig, ax = plt.subplots() # fig, ax = plt.subplots()
# Add the patch to the Axes # Add the patch to the Axes
@ -178,7 +178,7 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
plt.ylim([-1.1, lim]) plt.ylim([-1.1, lim])
plt.pause(0.01) plt.pause(0.01)
elif mode == "final": elif self.render_mode == "final":
if self._steps == 199 or self._is_collided: if self._steps == 199 or self._is_collided:
# fig, ax = plt.subplots() # fig, ax = plt.subplots()

3
fancy_gym/envs/mujoco/ant_jump/ant_jump.py
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@ -101,6 +101,7 @@ class AntJumpEnv(AntEnvCustomXML):
contact_force_range=(-1.0, 1.0), contact_force_range=(-1.0, 1.0),
reset_noise_scale=0.1, reset_noise_scale=0.1,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
**kwargs
): ):
self.current_step = 0 self.current_step = 0
self.max_height = 0 self.max_height = 0
@ -113,7 +114,7 @@ class AntJumpEnv(AntEnvCustomXML):
healthy_z_range=healthy_z_range, healthy_z_range=healthy_z_range,
contact_force_range=contact_force_range, contact_force_range=contact_force_range,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation, **kwargs)
def step(self, action): def step(self, action):
self.current_step += 1 self.current_step += 1

24
fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
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@ -36,7 +36,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
"render_fps": 50 "render_fps": 50
} }
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, frame_skip: int = 10, random_init: bool = False, **kwargs):
utils.EzPickle.__init__(**locals()) utils.EzPickle.__init__(**locals())
self._steps = 0 self._steps = 0
self.init_qpos_box_pushing = np.array([0., 0., 0., -1.5, 0., 1.5, 0., 0., 0., 0.6, 0.45, 0.0, 1., 0., 0., 0.]) self.init_qpos_box_pushing = np.array([0., 0., 0., -1.5, 0., 1.5, 0., 0., 0., 0.6, 0.45, 0.0, 1., 0., 0., 0.])
@ -58,7 +58,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
MujocoEnv.__init__(self, MujocoEnv.__init__(self,
model_path=os.path.join(os.path.dirname(__file__), "assets", "box_pushing.xml"), model_path=os.path.join(os.path.dirname(__file__), "assets", "box_pushing.xml"),
frame_skip=self.frame_skip, frame_skip=self.frame_skip,
observation_space=self.observation_space) observation_space=self.observation_space, **kwargs)
self.action_space = spaces.Box(low=-1, high=1, shape=(7,)) self.action_space = spaces.Box(low=-1, high=1, shape=(7,))
def step(self, action): def step(self, action):
@ -305,8 +305,8 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
class BoxPushingDense(BoxPushingEnvBase): class BoxPushingDense(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, **kwargs):
super(BoxPushingDense, self).__init__(frame_skip=frame_skip, random_init=random_init) super(BoxPushingDense, self).__init__(**kwargs)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat, def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action): rod_tip_pos, rod_quat, qpos, qvel, action):
joint_penalty = self._joint_limit_violate_penalty(qpos, joint_penalty = self._joint_limit_violate_penalty(qpos,
@ -329,8 +329,8 @@ class BoxPushingDense(BoxPushingEnvBase):
class BoxPushingTemporalSparse(BoxPushingEnvBase): class BoxPushingTemporalSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, **kwargs):
super(BoxPushingTemporalSparse, self).__init__(frame_skip=frame_skip, random_init=random_init) super(BoxPushingTemporalSparse, self).__init__(**kwargs)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat, def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action): rod_tip_pos, rod_quat, qpos, qvel, action):
@ -361,8 +361,8 @@ class BoxPushingTemporalSparse(BoxPushingEnvBase):
class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase): class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, **kwargs):
super(BoxPushingTemporalSpatialSparse, self).__init__(frame_skip=frame_skip, random_init=random_init) super(BoxPushingTemporalSpatialSparse, self).__init__(**kwargs)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat, def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action): rod_tip_pos, rod_quat, qpos, qvel, action):
@ -392,8 +392,8 @@ class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
class BoxPushingTemporalSpatialSparse2(BoxPushingEnvBase): class BoxPushingTemporalSpatialSparse2(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, **kwargs):
super(BoxPushingTemporalSpatialSparse2, self).__init__(frame_skip=frame_skip, random_init=random_init) super(BoxPushingTemporalSpatialSparse2, self).__init__(**kwargs)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat, def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action): rod_tip_pos, rod_quat, qpos, qvel, action):
@ -428,8 +428,8 @@ class BoxPushingTemporalSpatialSparse2(BoxPushingEnvBase):
class BoxPushingNoConstraintSparse(BoxPushingEnvBase): class BoxPushingNoConstraintSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False): def __init__(self, **kwargs):
super(BoxPushingNoConstraintSparse, self).__init__(frame_skip=frame_skip, random_init=random_init) super(BoxPushingNoConstraintSparse, self).__init__(**kwargs)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat, def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action): rod_tip_pos, rod_quat, qpos, qvel, action):

6
fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py
View File

@ -74,7 +74,8 @@ class HalfCheetahJumpEnv(HalfCheetahEnvCustomXML):
reset_noise_scale=0.1, reset_noise_scale=0.1,
context=True, context=True,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
max_episode_steps=100): max_episode_steps=100,
**kwargs):
self.current_step = 0 self.current_step = 0
self.max_height = 0 self.max_height = 0
# self.max_episode_steps = max_episode_steps # self.max_episode_steps = max_episode_steps
@ -85,7 +86,8 @@ class HalfCheetahJumpEnv(HalfCheetahEnvCustomXML):
forward_reward_weight=forward_reward_weight, forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight, ctrl_cost_weight=ctrl_cost_weight,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation,
**kwargs)
def step(self, action): def step(self, action):

4
fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py
View File

@ -115,6 +115,7 @@ class HopperJumpEnv(HopperEnvCustomXML):
reset_noise_scale=5e-3, reset_noise_scale=5e-3,
exclude_current_positions_from_observation=False, exclude_current_positions_from_observation=False,
sparse=False, sparse=False,
**kwargs
): ):
self.sparse = sparse self.sparse = sparse
@ -141,7 +142,8 @@ class HopperJumpEnv(HopperEnvCustomXML):
healthy_z_range=healthy_z_range, healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range, healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation,
**kwargs)
# increase initial height # increase initial height
self.init_qpos[1] = 1.5 self.init_qpos[1] = 1.5

6
fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py
View File

@ -29,7 +29,8 @@ class HopperJumpOnBoxEnv(HopperEnvCustomXML):
reset_noise_scale=5e-3, reset_noise_scale=5e-3,
context=True, context=True,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
max_episode_steps=250): max_episode_steps=250,
**kwargs):
self.current_step = 0 self.current_step = 0
self.max_height = 0 self.max_height = 0
self.max_episode_steps = max_episode_steps self.max_episode_steps = max_episode_steps
@ -50,7 +51,8 @@ class HopperJumpOnBoxEnv(HopperEnvCustomXML):
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file) xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy, super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale, healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation) exclude_current_positions_from_observation,
**kwargs)
def step(self, action): def step(self, action):

6
fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py
View File

@ -32,7 +32,8 @@ class HopperThrowEnv(HopperEnvCustomXML):
reset_noise_scale=5e-3, reset_noise_scale=5e-3,
context=True, context=True,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
max_episode_steps=250): max_episode_steps=250,
**kwargs):
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file) xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
self.current_step = 0 self.current_step = 0
self.max_episode_steps = max_episode_steps self.max_episode_steps = max_episode_steps
@ -57,7 +58,8 @@ class HopperThrowEnv(HopperEnvCustomXML):
healthy_z_range=healthy_z_range, healthy_z_range=healthy_z_range,
healthy_state_range=healthy_angle_range, healthy_state_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation,
**kwargs)
def step(self, action): def step(self, action):
self.current_step += 1 self.current_step += 1

6
fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py
View File

@ -36,7 +36,8 @@ class HopperThrowInBasketEnv(HopperEnvCustomXML):
context=True, context=True,
penalty=0.0, penalty=0.0,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
max_episode_steps=250): max_episode_steps=250,
**kwargs):
self.hit_basket_reward = hit_basket_reward self.hit_basket_reward = hit_basket_reward
self.current_step = 0 self.current_step = 0
self.max_episode_steps = max_episode_steps self.max_episode_steps = max_episode_steps
@ -65,7 +66,8 @@ class HopperThrowInBasketEnv(HopperEnvCustomXML):
healthy_z_range=healthy_z_range, healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range, healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation,
**kwargs)
def step(self, action): def step(self, action):

13
fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
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@ -34,7 +34,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
def __init__(self, ctxt_dim: int = 4, frame_skip: int = 4, def __init__(self, ctxt_dim: int = 4, frame_skip: int = 4,
goal_switching_step: int = None, goal_switching_step: int = None,
enable_artificial_wind: bool = False): enable_artificial_wind: bool = False, **kwargs):
utils.EzPickle.__init__(**locals()) utils.EzPickle.__init__(**locals())
self._steps = 0 self._steps = 0
@ -68,7 +68,8 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
MujocoEnv.__init__(self, MujocoEnv.__init__(self,
model_path=os.path.join(os.path.dirname(__file__), "assets", "xml", "table_tennis_env.xml"), model_path=os.path.join(os.path.dirname(__file__), "assets", "xml", "table_tennis_env.xml"),
frame_skip=frame_skip, frame_skip=frame_skip,
observation_space=self.observation_space) observation_space=self.observation_space,
**kwargs)
if ctxt_dim == 2: if ctxt_dim == 2:
self.context_bounds = CONTEXT_BOUNDS_2DIMS self.context_bounds = CONTEXT_BOUNDS_2DIMS
@ -275,11 +276,11 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
class TableTennisWind(TableTennisEnv): class TableTennisWind(TableTennisEnv):
def __init__(self, ctxt_dim: int = 4, frame_skip: int = 4): def __init__(self, ctxt_dim: int = 4, frame_skip: int = 4, **kwargs):
self.observation_space = spaces.Box( self.observation_space = spaces.Box(
low=-np.inf, high=np.inf, shape=(22,), dtype=np.float64 low=-np.inf, high=np.inf, shape=(22,), dtype=np.float64
) )
super().__init__(ctxt_dim=ctxt_dim, frame_skip=frame_skip, enable_artificial_wind=True) super().__init__(ctxt_dim=ctxt_dim, frame_skip=frame_skip, enable_artificial_wind=True, **kwargs)
def _get_obs(self): def _get_obs(self):
obs = np.concatenate([ obs = np.concatenate([
@ -297,5 +298,5 @@ class TableTennisWind(TableTennisEnv):
class TableTennisGoalSwitching(TableTennisEnv): class TableTennisGoalSwitching(TableTennisEnv):
def __init__(self, frame_skip: int = 4, goal_switching_step: int = 99): def __init__(self, frame_skip: int = 4, goal_switching_step: int = 99, **kwargs):
super().__init__(frame_skip=frame_skip, goal_switching_step=goal_switching_step) super().__init__(frame_skip=frame_skip, goal_switching_step=goal_switching_step, **kwargs)

6
fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py
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@ -97,7 +97,8 @@ class Walker2dJumpEnv(Walker2dEnvCustomXML):
reset_noise_scale=5e-3, reset_noise_scale=5e-3,
penalty=0, penalty=0,
exclude_current_positions_from_observation=True, exclude_current_positions_from_observation=True,
max_episode_steps=300): max_episode_steps=300,
**kwargs):
self.current_step = 0 self.current_step = 0
self.max_episode_steps = max_episode_steps self.max_episode_steps = max_episode_steps
self.max_height = 0 self.max_height = 0
@ -112,7 +113,8 @@ class Walker2dJumpEnv(Walker2dEnvCustomXML):
healthy_z_range=healthy_z_range, healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range, healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale, reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation) exclude_current_positions_from_observation=exclude_current_positions_from_observation,
**kwargs)
def step(self, action): def step(self, action):
self.current_step += 1 self.current_step += 1

2
test/test_black_box.py
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@ -41,7 +41,7 @@ class ToyEnv(gym.Env):
obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {} obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {}
return obs, reward, terminated, truncated, info return obs, reward, terminated, truncated, info
def render(self, mode="human"): def render(self):
pass pass

2
test/test_fancy_registry.py
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@ -33,7 +33,7 @@ class ToyEnv(gym.Env):
obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {} obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {}
return obs, reward, terminated, truncated, info return obs, reward, terminated, truncated, info
def render(self, mode="human"): def render(self):
pass pass

2
test/test_replanning_sequencing.py
View File

@ -37,7 +37,7 @@ class ToyEnv(gym.Env):
obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {} obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {}
return obs, reward, terminated, truncated, info return obs, reward, terminated, truncated, info
def render(self, mode="human"): def render(self):
pass pass