From 9635e71cce55085d823cfd2a5809b9dbddb94f7c Mon Sep 17 00:00:00 2001
From: Mattias Ljungstrom <mattias.ljungstrom@gmail.com>
Date: Sun, 28 Apr 2019 17:57:59 +0200
Subject: [PATCH] level gen: enforce min/max distances
---
flatland/envs/rail_env.py | 36 +++++++++++++++++++++++++++++++-----
1 file changed, 31 insertions(+), 5 deletions(-)
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 86582f5..8132d93 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -235,7 +235,11 @@ def connect_rail(rail_trans, rail_array, start, end):
current_dir = new_dir
-def complex_rail_generator(nr_start_goal=1, min_dist=0, max_dist=99999, seed=0):
+def distance_on_rail(pos1, pos2):
+ return abs(pos1[0] - pos2[0]) + abs(pos1[1] - pos2[1])
+
+
+def complex_rail_generator(nr_start_goal=1, min_dist=2, max_dist=99999, seed=0):
"""
Parameters
-------
@@ -295,19 +299,41 @@ def complex_rail_generator(nr_start_goal=1, min_dist=0, max_dist=99999, seed=0):
# - return transition map + list of [start, goal] points
#
+ # step 1:
start_goal = []
for _ in range(nr_start_goal):
- start = (np.random.randint(0, width), np.random.randint(0, height))
- goal = (np.random.randint(0, height), np.random.randint(0, height))
- # TODO: validate closeness with existing points
- # TODO: make sure min/max distance condition is met
+ sanity_max = 9000
+ for _ in range(sanity_max):
+ start = (np.random.randint(0, width), np.random.randint(0, height))
+ goal = (np.random.randint(0, height), np.random.randint(0, height))
+ # check min/max distance
+ dist_sg = distance_on_rail(start, goal)
+ if dist_sg < min_dist:
+ continue
+ if dist_sg > max_dist:
+ continue
+ # check distance to existing points
+ sg_new = [start, goal]
+ def check_all_dist(sg_new):
+ for sg in start_goal:
+ for i in range(2):
+ for j in range(2):
+ dist = distance_on_rail(sg_new[i], sg[j])
+ if dist < 2:
+ # print("too close:", dist, sg_new[i], sg[j])
+ return False
+ return True
+ if check_all_dist(sg_new):
+ break
start_goal.append([start, goal])
+ # step 3:
for sg in start_goal:
connect_rail(rail_trans, rail_array, sg[0], sg[1])
return_rail = GridTransitionMap(width=width, height=height, transitions=rail_trans)
return_rail.grid = rail_array
+ # TODO: return start_goal
return return_rail
return generator
--
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