diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index a1ad9a85d9a41f57c317a0b4b0bc61796e4e0f4a..da8cacef2a3e9972ef4b90094c59f448ca07bc37 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -13,7 +13,7 @@ np.random.seed(1)
# Training on simple small tasks is the best way to get familiar with the environment
# Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
+stochastic_data = {'prop_malfunction': 1., # Percentage of defective agents
'malfunction_rate': 30, # Rate of malfunction occurence
'min_duration': 3, # Minimal duration of malfunction
'max_duration': 20 # Max duration of malfunction
@@ -31,11 +31,11 @@ speed_ration_map = {1.: 0.25, # Fast passenger train
env = RailEnv(width=50,
height=50,
rail_generator=sparse_rail_generator(num_cities=25, # Number of cities in map (where train stations are)
- num_intersections=0, # Number of intersections (no start / target)
+ num_intersections=10, # Number of intersections (no start / target)
num_trainstations=50, # Number of possible start/targets on map
min_node_dist=3, # Minimal distance of nodes
- node_radius=2, # Proximity of stations to city center
- num_neighb=3, # Number of connections to other cities/intersections
+ node_radius=4, # Proximity of stations to city center
+ num_neighb=4, # Number of connections to other cities/intersections
seed=15, # Random seed
grid_mode=True,
enhance_intersection=False
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 8486fc7f5f024023b44a5c6139bdb0d628e12303..4926fb1fc153f25ade27f59db5d546ada8804c15 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -266,7 +266,8 @@ class RailEnv(Environment):
def _agent_malfunction(self, agent):
# Decrease counter for next event
- agent.malfunction_data['next_malfunction'] -= 1
+ if agent.malfunction_data['malfunction_rate'] > 0:
+ agent.malfunction_data['next_malfunction'] -= 1
# Only agents that have a positive rate for malfunctions and are not currently broken are considered
if agent.malfunction_data['malfunction_rate'] > 0 >= agent.malfunction_data['malfunction']:
@@ -328,15 +329,22 @@ class RailEnv(Environment):
# The train is broken
if agent.malfunction_data['malfunction'] > 0:
- agent.malfunction_data['malfunction'] -= 1
+ # Last step of malfunction --> Agent starts moving again
+ if agent.malfunction_data['malfunction'] < 2:
+ agent.malfunction_data['malfunction'] -= 1
+ self.agents[i_agent].moving = True
+ action_dict[i_agent] = RailEnvActions.DO_NOTHING
- # Broken agents are stopped
- self.rewards_dict[i_agent] += step_penalty # * agent.speed_data['speed']
- self.agents[i_agent].moving = False
- action_dict[i_agent] = RailEnvActions.DO_NOTHING
+ else:
+ agent.malfunction_data['malfunction'] -= 1
- # Nothing left to do with broken agent
- continue
+ # Broken agents are stopped
+ self.rewards_dict[i_agent] += step_penalty # * agent.speed_data['speed']
+ self.agents[i_agent].moving = False
+ action_dict[i_agent] = RailEnvActions.DO_NOTHING
+
+ # Nothing left to do with broken agent
+ continue
if action_dict[i_agent] < 0 or action_dict[i_agent] > len(RailEnvActions):
print('ERROR: illegal action=', action_dict[i_agent],
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 39796515f73c7702ba4dc162301a63b0186dc1d3..c23593463c2b679cfd09fcbcf390c3d5a05acde4 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -563,7 +563,6 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
node_positions = []
city_positions = []
intersection_positions = []
-
# Evenly distribute cities and intersections
if grid_mode:
tot_num_node = num_intersections + num_cities
@@ -572,10 +571,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
nodes_per_col = int(np.ceil(tot_num_node / nodes_per_row))
x_positions = np.linspace(node_radius, height - node_radius, nodes_per_row, dtype=int)
y_positions = np.linspace(node_radius, width - node_radius, nodes_per_col, dtype=int)
-
- fraction = 0
- city_fraction = num_cities / tot_num_node
- step = np.gcd(num_intersections, num_cities) / tot_num_node
+ city_idx = np.random.choice(np.arange(tot_num_node), num_cities)
for node_idx in range(num_cities + num_intersections):
to_close = True
@@ -608,10 +604,9 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
warnings.warn("Could not set nodes, please change initial parameters!!!!")
break
else:
- fraction = (fraction + step) % 1.
x_tmp = x_positions[node_idx % nodes_per_row]
y_tmp = y_positions[node_idx // nodes_per_row]
- if len(city_positions) < num_cities and fraction < city_fraction:
+ if node_idx in city_idx:
city_positions.append((x_tmp, y_tmp))
else:
intersection_positions.append((x_tmp, y_tmp))