From 44635204283dd5a026d067d1168d50f8742ade8c Mon Sep 17 00:00:00 2001
From: MLErik <baerenjesus@gmail.com>
Date: Wed, 25 Sep 2019 14:27:26 -0400
Subject: [PATCH] fixed bug in randomly placed cities
---
examples/flatland_2_0_example.py | 4 ++--
flatland/envs/rail_generators.py | 31 ++++++++++++++++---------------
2 files changed, 18 insertions(+), 17 deletions(-)
diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index 339945f3..2334450c 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -35,11 +35,11 @@ env = RailEnv(width=50,
rail_generator=sparse_rail_generator(num_cities=9, # Number of cities in map (where train stations are)
num_intersections=0, # Number of intersections (no start / target)
num_trainstations=15, # Number of possible start/targets on map
- min_node_dist=3, # Minimal distance of nodes
+ min_node_dist=10, # Minimal distance of nodes
node_radius=4, # Proximity of stations to city center
num_neighb=2, # Number of connections to other cities/intersections
seed=15, # Random seed
- grid_mode=True,
+ grid_mode=False,
enhance_intersection=False
),
schedule_generator=sparse_schedule_generator(),
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index f3d7b889..c0721f62 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -604,26 +604,30 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
# Start at some node
current_node = np.random.randint(len(available_nodes_full))
node_stack = [current_node]
+ open_nodes = np.copy(available_nodes_full)
allowed_connections = num_neighb
- first_node = True
i = 0
boarder_connections = set()
- while len(node_stack) > 0:
- current_node = node_stack[0]
- delete_idx = np.where(available_nodes_full == current_node)
- available_nodes_full = np.delete(available_nodes_full, delete_idx, 0)
+ while len(open_nodes) > 0:
+ if len(node_stack) > 0:
+ current_node = node_stack[0]
+ else:
+ current_node = np.random.choice(open_nodes)
+ node_stack.append(current_node)
+ delete_idx = np.where(open_nodes == current_node)
+ open_nodes = np.delete(open_nodes, delete_idx, 0)
# Priority city to intersection connections
if current_node < _num_cities and len(available_intersections) > 0:
available_nodes = available_intersections
delete_idx = np.where(available_cities == current_node)
- available_cities = np.delete(available_cities, delete_idx, 0)
+ # available_cities = np.delete(available_cities, delete_idx, 0)
# Priority intersection to city connections
elif current_node >= _num_cities and len(available_cities) > 0:
available_nodes = available_cities
delete_idx = np.where(available_intersections == current_node)
- available_intersections = np.delete(available_intersections, delete_idx, 0)
+ # available_intersections = np.delete(available_intersections, delete_idx, 0)
# If no options possible connect to whatever node is still available
else:
@@ -637,18 +641,15 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
# Set number of neighboring nodes
if len(available_nodes) >= allowed_connections:
- connected_neighb_idx = available_nodes[:allowed_connections]
+ connected_neighb_idx = available_nodes[1:allowed_connections + 1]
else:
connected_neighb_idx = available_nodes
+
print(current_node, connected_neighb_idx)
- # Less connections for subsequent nodes
- if first_node:
- allowed_connections -= 1
- first_node = False
# Connect to the neighboring nodes
for neighb in connected_neighb_idx:
- if neighb not in node_stack:
+ if neighb not in node_stack and neighb in open_nodes:
node_stack.append(neighb)
dist_from_center = distance_on_rail(node_positions[current_node], node_positions[neighb])
@@ -824,8 +825,8 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
tries = 0
while to_close:
- x_tmp = node_radius + np.random.randint(height - node_radius - 1)
- y_tmp = node_radius + np.random.randint(width - node_radius - 1)
+ x_tmp = node_radius + np.random.randint(height - 2 * node_radius - 1)
+ y_tmp = node_radius + np.random.randint(width - 2 * node_radius - 1)
to_close = False
# Check distance to cities
--
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