Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Change way action space is discretized #205

Merged
merged 2 commits into from
Sep 13, 2023
+43 −43
Merged

Change way action space is discretized #205

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
221b555
Change way action space is discretized
galleon Sep 18, 2022
1ff4a69
Changing iteration scheme in loop
galleon Sep 13, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
86 changes: 43 additions & 43 deletions skdecide/hub/domain/gym/gym.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,8 @@
namedtuple,
)
from copy import deepcopy
from itertools import product
from math import pi, tan
from typing import Any, Callable, List, Optional

import gym
Expand Down Expand Up @@ -700,30 +702,50 @@ def _discretize_action_space(
self, action_space: gym.spaces.Space
) -> D.T_agent[Space[D.T_event]]:
if isinstance(action_space, gym.spaces.box.Box):
nb_elements = 1
for dim in action_space.shape:
nb_elements *= dim
actions = []
for l, h in np.nditer([action_space.low, action_space.high]):
if l == -float("inf") or h == float("inf"):
actions.append(
[
gym.spaces.box.Box(low=l, high=h).sample()
for i in range(self._discretization_factor)
ticks = []

unbounded = ~action_space.bounded_below & ~action_space.bounded_above
upp_bounded = ~action_space.bounded_below & action_space.bounded_above
low_bounded = action_space.bounded_below & ~action_space.bounded_above
bounded = action_space.bounded_below & action_space.bounded_above

it = np.nditer(action_space.low, flags=["multi_index"])
for _ in it:
index = it.multi_index

if unbounded[index]:
l = [
tan(0.5 * pi * x)
for x in np.linspace(-1, 1, self._discretization_factor + 2)[
1:-1
]
]
elif upp_bounded[index]:
l = [
action_space.high[index] + tan(0.5 * pi * x)
for x in np.linspace(
-1, 0, self._discretization_factor + 1, endpoint=True
)[1:]
]
elif low_bounded[index]:
l = [
action_space.low[index] + tan(0.5 * pi * x)
for x in np.linspace(
0, 1, self._discretization_factor + 1, endpoint=True
)[:-1]
]
elif bounded[index]:
l = np.linspace(
action_space.low[index],
action_space.high[index],
self._discretization_factor,
)
else:
actions.append(
[
l + ((h - l) / (self._discretization_factor - 1)) * i
for i in range(self._discretization_factor)
]
)
alist = []
self._generate_box_action_combinations(
actions, action_space.shape, action_space.dtype, 0, [], alist
)
return ListSpace(alist)
raise ValueError("Invalid case")

ticks.append(l)

return ListSpace(product(*ticks))
elif isinstance(action_space, gym.spaces.discrete.Discrete):
return ListSpace([i for i in range(action_space.n)])
elif isinstance(action_space, gym.spaces.multi_discrete.MultiDiscrete):
Expand Down Expand Up @@ -781,28 +803,6 @@ def _discretize_action_space(
"Unknown Gym space element of type " + str(type(action_space))
)

def _generate_box_action_combinations(
self, actions, shape, dtype, index, alist, rlist
):
if index < len(actions):
for a in actions[index]:
clist = list(alist)
clist.append(a)
self._generate_box_action_combinations(
actions, shape, dtype, index + 1, clist, rlist
)
else:
ar = np.ndarray(shape=shape, dtype=dtype)
if len(shape) == 1:
ar[0] = alist[0]
else:
k = 0
for (i,) in np.nditer(ar, op_flags=["readwrite"]):
print(str(i))
i = alist[k]
k += 1
rlist += [ar]


class D(
Domain,
Expand Down