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adjusted classic control environments to new interface

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This commit is contained in:
ottofabian
2021-05-12 17:48:57 +02:00
parent 95e9b8be47
commit 6ae195962c
14 changed files with 535 additions and 489 deletions
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alr_envs/utils/legacy/utils.py
+23 -34
View File
@@ -49,13 +49,13 @@ def make_holereacher_env(rank, seed=0):
"""
def _init():
_env = hr.HoleReacher(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.25,
hole_depth=1,
hole_x=2,
collision_penalty=100)
_env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.25,
hole_depth=1,
hole_x=2,
collision_penalty=100)
_env = DmpWrapper(_env,
num_dof=5,
@@ -89,13 +89,13 @@ def make_holereacher_fix_goal_env(rank, seed=0):
"""
def _init():
_env = hr.HoleReacher(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.15,
hole_depth=1,
hole_x=1,
collision_penalty=100)
_env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.15,
hole_depth=1,
hole_x=1,
collision_penalty=100)
_env = DmpWrapper(_env,
num_dof=5,
@@ -129,27 +129,16 @@ def make_holereacher_env_pmp(rank, seed=0):
"""
def _init():
_env = hr.HoleReacher(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.15,
hole_depth=1,
hole_x=1,
collision_penalty=1000)
_env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False,
allow_wall_collision=False,
hole_width=0.15,
hole_depth=1,
hole_x=1,
collision_penalty=1000)
_env = DetPMPWrapper(_env,
num_dof=5,
num_basis=5,
width=0.02,
policy_type="velocity",
start_pos=_env._start_pos,
duration=2,
post_traj_time=0,
dt=_env.dt,
weights_scale=0.2,
zero_start=True,
zero_goal=False
)
_env = DetPMPWrapper(_env, num_dof=5, num_basis=5, width=0.02, duration=2, dt=_env.dt, post_traj_time=0,
policy_type="velocity", weights_scale=0.2, zero_start=True, zero_goal=False)
_env.seed(seed + rank)
return _env
alr_envs/utils/mps/detpmp_wrapper.py
+7 -7
View File
@@ -7,22 +7,22 @@ from alr_envs.utils.mps.mp_wrapper import MPWrapper
class DetPMPWrapper(MPWrapper):
def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, start_pos=None, duration: int = 1,
dt: float = 0.01, post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, duration: int = 1, dt: float = 0.01,
post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
zero_start: bool = False, zero_goal: bool = False, **mp_kwargs):
# self.duration = duration # seconds
self.duration = duration # seconds
super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis,
width=width, start_pos=start_pos, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
width=width, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
self.dt = dt
action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof))
self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32)
self.start_pos = start_pos
self.dt = dt
def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, width: float = None,
start_pos: np.ndarray = None, zero_start: bool = False, zero_goal: bool = False):
zero_start: bool = False, zero_goal: bool = False):
pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01,
zero_start=zero_start, zero_goal=zero_goal)
alr_envs/utils/mps/dmp_wrapper.py
+1 -1
View File
@@ -63,7 +63,7 @@ class DmpWrapper(MPWrapper):
goal_pos = params[0, -self.mp.num_dimensions:] # [num_dof]
params = params[:, :-self.mp.num_dimensions] # [1,num_dof]
else:
goal_pos = self.env.goal_pos # self.mp.dmp_goal_pos.flatten()
goal_pos = self.env.goal_pos
assert goal_pos is not None
weight_matrix = np.reshape(params, self.mp.dmp_weights.shape) # [num_basis, num_dof]
alr_envs/utils/mps/mp_environments.py
+1 -1
View File
@@ -9,7 +9,7 @@ class MPEnv(gym.Env):
@property
@abstractmethod
def corrected_obs_index(self):
def active_obs(self):
"""Returns boolean value for each observation entry
whether the observation is returned by the DMP for the contextual case or not.
This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
alr_envs/utils/mps/mp_wrapper.py
+8 -3
View File
@@ -13,6 +13,12 @@ class MPWrapper(gym.Wrapper, ABC):
policy_type: str = None, weights_scale: float = 1., render_mode: str = None, **mp_kwargs):
super().__init__(env)
# adjust observation space to reduce version
obs_sp = self.env.observation_space
self.observation_space = gym.spaces.Box(low=obs_sp.low[self.env.active_obs],
high=obs_sp.high[self.env.active_obs],
dtype=obs_sp.dtype)
assert dt is not None # this should never happen as MPWrapper is a base class
self.post_traj_steps = int(post_traj_time / dt)
@@ -51,8 +57,7 @@ class MPWrapper(gym.Wrapper, ABC):
self.env.configure(context)
def reset(self):
obs = self.env.reset()
return obs[self.env]
return self.env.reset()[self.env.active_obs]
def step(self, action: np.ndarray):
""" This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
@@ -82,7 +87,7 @@ class MPWrapper(gym.Wrapper, ABC):
break
done = True
return obs, rewards, done, info
return obs[self.env.active_obs], rewards, done, info
def render(self, mode='human', **kwargs):
"""Only set render options here, such that they can be used during the rollout.