distance = distance + walls.numel() * walls
value = distance.new_full((4,) + distance.size(), walls.numel())
- value[0, :, 1:] = distance[:, :-1]
- value[1, :, :-1] = distance[:, 1:]
- value[2, 1:, :] = distance[:-1, :]
- value[3, :-1, :] = distance[1:, :]
+ value[0, :, 1:] = distance[:, :-1] # <
+ value[1, :, :-1] = distance[:, 1:] # >
+ value[2, 1:, :] = distance[:-1, :] # ^
+ value[3, :-1, :] = distance[1:, :] # v
proba = (value.min(dim=0)[0][None] == value).float()
proba = proba / proba.sum(dim=0)[None]
def stationary_densities(mazes, policies):
+ policies = policies * (mazes != v_goal)[:, None]
start = (mazes == v_start).nonzero(as_tuple=True)
- probas = mazes.new_zeros(mazes.size())
+ probas = mazes.new_zeros(mazes.size(), dtype=torch.float32)
pred_probas = probas.clone()
probas[start] = 1.0
while not pred_probas.equal(probas):
pred_probas.copy_(probas)
probas.zero_()
- probas[:, 1:, :] = pred_probas[:, :-1, :] * policies[:, 0, :-1, :]
- probas[:, :-1, :] = pred_probas[:, 1:, :] * policies[:, 1, 1:, :]
- probas[:, :, 1:] = pred_probas[:, :, :-1] * policies[:, 2, :, :-1]
- probas[:, :, :-1] = pred_probas[:, :, 1:] * policies[:, 3, :, 1:]
+ probas[:, 1:, :] += pred_probas[:, :-1, :] * policies[:, 3, :-1, :]
+ probas[:, :-1, :] += pred_probas[:, 1:, :] * policies[:, 2, 1:, :]
+ probas[:, :, 1:] += pred_probas[:, :, :-1] * policies[:, 1, :, :-1]
+ probas[:, :, :-1] += pred_probas[:, :, 1:] * policies[:, 0, :, 1:]
probas[start] = 1.0
return probas
target_paths=None,
predicted_paths=None,
score_paths=None,
+ score_truth=None,
path_correct=None,
):
colors = torch.tensor(
colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
)
+ if score_truth is not None:
+ score_truth = score_truth.cpu()
+ c_score_truth = score_truth.unsqueeze(1).expand(-1, 3, -1, -1)
+ c_score_truth = (
+ c_score_truth * colors[4].reshape(1, 3, 1, 1)
+ + (1 - c_score_truth) * colors[0].reshape(1, 3, 1, 1)
+ ).long()
+ c_mazes = (mazes.unsqueeze(1) != v_empty) * c_mazes + (
+ mazes.unsqueeze(1) == v_empty
+ ) * c_score_truth
+
imgs = c_mazes.unsqueeze(1)
if target_paths is not None: