diff --git a/README.md b/README.md
index 14102defb5f7508d9ceb04e3cd0f1a441c4ca172..0f621c1b0c29f3dfce2c0e6b892106dbc9ff6919 100644
--- a/README.md
+++ b/README.md
@@ -106,7 +106,7 @@ env = RailEnv(
nr_extra=1,
min_dist=8,
max_dist=99999,
- seed=0),
+ seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=NUMBER_OF_AGENTS)
diff --git a/docs/specifications/railway.md b/docs/specifications/railway.md
index 04867f08f4b3a2948dd09983d552d5f33222cf4f..ec707f871b31a130f7a129e77042d02c31ddd300 100644
--- a/docs/specifications/railway.md
+++ b/docs/specifications/railway.md
@@ -380,7 +380,7 @@ ScheduleGenerator = Callable[[GridTransitionMap, int, Optional[Any]], ScheduleGe
We can then produce `RailGenerator`s by currying:
```python
def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2, min_node_dist=20, node_radius=2,
- num_neighb=3, grid_mode=False, enhance_intersection=False, seed=0):
+ num_neighb=3, grid_mode=False, enhance_intersection=False, seed=1):
def generator(width, height, num_agents, num_resets=0):
diff --git a/docs/tutorials/01_gettingstarted.rst b/docs/tutorials/01_gettingstarted.rst
index 2be0fee229c93f2aee215cf1e459dd8fc8f92ebb..e3a2f41aca4714150e96b725d1b01da0f5e20958 100644
--- a/docs/tutorials/01_gettingstarted.rst
+++ b/docs/tutorials/01_gettingstarted.rst
@@ -145,7 +145,7 @@ Next we configure the difficulty of our task by modifying the complex_rail_gener
nr_extra=10,
min_dist=10,
max_dist=99999,
- seed=0),
+ seed=1),
number_of_agents=5)
The difficulty of a railway network depends on the dimensions (`width` x `height`) and the number of agents in the network.
diff --git a/docs/tutorials/02_observationbuilder.rst b/docs/tutorials/02_observationbuilder.rst
index f6e718ab156a3972a57f4693367d5f191c8fcdd0..8afcf71043cdff6239c8944ce243d1f1407fc718 100644
--- a/docs/tutorials/02_observationbuilder.rst
+++ b/docs/tutorials/02_observationbuilder.rst
@@ -118,7 +118,7 @@ Note that this simple strategy fails when multiple agents are present, as each a
env = RailEnv(width=7,
height=7,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, \
- min_dist=8, max_dist=99999, seed=0),
+ min_dist=8, max_dist=99999, seed=1),
number_of_agents=2,
obs_builder_object=SingleAgentNavigationObs())
@@ -267,7 +267,7 @@ We can then use this new observation builder and the renderer to visualize the o
# Initiate Environment
env = RailEnv(width=10,
height=10,
- rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=1, min_dist=8, max_dist=99999, seed=0),
+ rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=1, min_dist=8, max_dist=99999, seed=1),
number_of_agents=3,
obs_builder_object=CustomObsBuilder)
diff --git a/docs/tutorials/05_multispeed.md b/docs/tutorials/05_multispeed.md
index 99db7ee6a275fe317c891cdebfc8d6aaf99d0b8c..118d4c5957a9b0219cc4ec1f897e2a322d8a248e 100644
--- a/docs/tutorials/05_multispeed.md
+++ b/docs/tutorials/05_multispeed.md
@@ -64,7 +64,7 @@ ScheduleGenerator = Callable[[GridTransitionMap, int, Optional[Any]], ScheduleGe
We can then produce `RailGenerator`s by currying:
```python
def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2, min_node_dist=20, node_radius=2,
- num_neighb=3, grid_mode=False, enhance_intersection=False, seed=0):
+ num_neighb=3, grid_mode=False, enhance_intersection=False, seed=1):
def generator(width, height, num_agents, num_resets=0):
diff --git a/examples/Simple_Realistic_Railway_Generator.py b/examples/Simple_Realistic_Railway_Generator.py
index 9124edd1cae6e7d1f2f3b909c721a8f4ffd35ca3..d4f013d56e0cbac5462cef3fab57ad12450ab0ad 100644
--- a/examples/Simple_Realistic_Railway_Generator.py
+++ b/examples/Simple_Realistic_Railway_Generator.py
@@ -201,7 +201,7 @@ def realistic_rail_generator(max_num_cities=5,
nbr_of_switches_per_station_track=2,
connect_max_nbr_of_shortes_city=4,
do_random_connect_stations=False,
- seed=0,
+ seed=1,
print_out_info=True) -> RailGenerator:
"""
This is a level generator which generates a realistic rail configurations
diff --git a/examples/custom_observation_example_02_SingleAgentNavigationObs.py b/examples/custom_observation_example_02_SingleAgentNavigationObs.py
index 3c10e415155429aa1464c554cdeb9ee1309780f2..aa9da8494b4a7376a3468c16618e5459344a37f3 100644
--- a/examples/custom_observation_example_02_SingleAgentNavigationObs.py
+++ b/examples/custom_observation_example_02_SingleAgentNavigationObs.py
@@ -75,7 +75,7 @@ def main(args):
env = RailEnv(width=7,
height=7,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999,
- seed=0),
+ seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=1,
obs_builder_object=SingleAgentNavigationObs())
diff --git a/examples/custom_observation_example_03_ObservePredictions.py b/examples/custom_observation_example_03_ObservePredictions.py
index 97fac09c8d222b11cc8e8cc3ab4f76c75a37a95b..7af7499af8ffc5900a87d7e543310aab2a6df7f9 100644
--- a/examples/custom_observation_example_03_ObservePredictions.py
+++ b/examples/custom_observation_example_03_ObservePredictions.py
@@ -125,7 +125,7 @@ def main(args):
env = RailEnv(width=10,
height=10,
rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=1, min_dist=8, max_dist=99999,
- seed=0),
+ seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=3,
obs_builder_object=custom_obs_builder)
diff --git a/examples/debugging_example_DELETE.py b/examples/debugging_example_DELETE.py
index 1f094c09b6a929b7e862bc42afffd1da38e52e73..9de330c9bd17f8c07acb931b1ca4eff689c798a7 100644
--- a/examples/debugging_example_DELETE.py
+++ b/examples/debugging_example_DELETE.py
@@ -57,7 +57,7 @@ class SingleAgentNavigationObs(ObservationBuilder):
env = RailEnv(width=14,
height=14,
- rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999, seed=0),
+ rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999, seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=2,
obs_builder_object=SingleAgentNavigationObs())
diff --git a/examples/simple_example_3.py b/examples/simple_example_3.py
index 61b83c3b9ec2d5f63203897aa52c87d27c4460bd..f294279d33b4f5ac7bcdd67202d1b09e65fcab29 100644
--- a/examples/simple_example_3.py
+++ b/examples/simple_example_3.py
@@ -13,7 +13,7 @@ np.random.seed(1)
env = RailEnv(width=7,
height=7,
- rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999, seed=0),
+ rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999, seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=2,
obs_builder_object=TreeObsForRailEnv(max_depth=2))
diff --git a/examples/training_example.py b/examples/training_example.py
index 8b42586b06d90bef44fe94ca6817fb0a84824aac..9d6631719db74ceaa8ceef9352345b2ddbeb99fd 100644
--- a/examples/training_example.py
+++ b/examples/training_example.py
@@ -16,7 +16,7 @@ TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictor
LocalGridObs = LocalObsForRailEnv(view_height=10, view_width=2, center=2)
env = RailEnv(width=20,
height=20,
- rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=2, min_dist=8, max_dist=99999, seed=0),
+ rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=2, min_dist=8, max_dist=99999, seed=1),
schedule_generator=complex_schedule_generator(),
obs_builder_object=TreeObservation,
number_of_agents=3)
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index aa8e48023694c96941b59d6f78b3fd93edf81e9e..ae3169f298ddd686d834d4a8bd571f4403dbbb79 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -118,7 +118,7 @@ class RailEnv(Environment):
max_episode_steps=None,
stochastic_data=None,
remove_agents_at_target=False,
- random_seed=None
+ random_seed=1
):
"""
Environment init.
@@ -160,7 +160,6 @@ class RailEnv(Environment):
self.rail_generator: RailGenerator = rail_generator
self.schedule_generator: ScheduleGenerator = schedule_generator
- self.rail_generator = rail_generator
self.rail: Optional[GridTransitionMap] = None
self.width = width
self.height = height
@@ -188,7 +187,7 @@ class RailEnv(Environment):
self.distance_map = DistanceMap(self.agents, self.height, self.width)
self.action_space = [1]
-
+
self._seed()
self._seed()
@@ -217,7 +216,7 @@ class RailEnv(Environment):
# Uniform distribution parameters for malfunction duration
self.min_number_of_steps_broken = malfunction_min_duration
self.max_number_of_steps_broken = malfunction_max_duration
- # Rest environment
+ # Reset environment
self.reset()
self.num_resets = 0 # yes, set it to zero again!
@@ -354,18 +353,18 @@ class RailEnv(Environment):
# If counter has come to zero --> Agent has malfunction
# set next malfunction time and duration of current malfunction
if agent.malfunction_data['malfunction_rate'] > 0 >= agent.malfunction_data['malfunction'] and \
- agent.malfunction_data['next_malfunction'] <= 0:
+ agent.malfunction_data['next_malfunction'] <= 0:
# Increase number of malfunctions
agent.malfunction_data['nr_malfunctions'] += 1
# Next malfunction in number of stops
next_breakdown = int(
- self.np_random.exponential(scale=agent.malfunction_data['malfunction_rate']))
+ self._exp_distirbution_synced(rate=agent.malfunction_data['malfunction_rate']))
agent.malfunction_data['next_malfunction'] = next_breakdown
# Duration of current malfunction
num_broken_steps = self.np_random.randint(self.min_number_of_steps_broken,
- self.max_number_of_steps_broken + 1) + 1
+ self.max_number_of_steps_broken + 1) + 1
agent.malfunction_data['malfunction'] = num_broken_steps
agent.malfunction_data['moving_before_malfunction'] = agent.moving
@@ -500,7 +499,7 @@ class RailEnv(Environment):
self.rewards_dict[i_agent] += self.stop_penalty
if not agent.moving and not (
- action == RailEnvActions.DO_NOTHING or action == RailEnvActions.STOP_MOVING):
+ action == RailEnvActions.DO_NOTHING or action == RailEnvActions.STOP_MOVING):
# Allow agent to start with any forward or direction action
agent.moving = True
self.rewards_dict[i_agent] += self.start_penalty
@@ -726,7 +725,7 @@ class RailEnv(Environment):
return msgpack.packb(msg_data, use_bin_type=True)
- def save(self, filename,save_distance_maps=False):
+ def save(self, filename, save_distance_maps=False):
if save_distance_maps == True:
if self.distance_map.get() is not None:
if len(self.distance_map.get()) > 0:
@@ -754,3 +753,14 @@ class RailEnv(Environment):
from importlib_resources import read_binary
load_data = read_binary(package, resource)
self.set_full_state_msg(load_data)
+
+ def _exp_distirbution_synced(self, rate):
+ """
+ Generates sample from exponential distribution
+ We need this to guarantee synchronity between different instances with same seed.
+ :param rate:
+ :return:
+ """
+ u = self.np_random.rand()
+ x = - np.log(1 - u) * rate
+ return x
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 242e11d73797b2dfdcea0efe7a079dcc066ff05e..ced33635a68948f5e91cf2147972caafc7d76334 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -40,7 +40,7 @@ def complex_rail_generator(nr_start_goal=1,
nr_extra=100,
min_dist=20,
max_dist=99999,
- seed=0) -> RailGenerator:
+ seed=1) -> RailGenerator:
"""
complex_rail_generator
@@ -272,7 +272,7 @@ def rail_from_grid_transition_map(rail_map) -> RailGenerator:
return generator
-def random_rail_generator(cell_type_relative_proportion=[1.0] * 11, seed=0) -> RailGenerator:
+def random_rail_generator(cell_type_relative_proportion=[1.0] * 11, seed=1) -> RailGenerator:
"""
Dummy random level generator:
- fill in cells at random in [width-2, height-2]
diff --git a/tests/test_flatland_envs_rail_env.py b/tests/test_flatland_envs_rail_env.py
index e0281bb0d3f21a8e25fdff86ade5bed05f5dea13..7c8a65fd6501689a0911872a00150a87a082805d 100644
--- a/tests/test_flatland_envs_rail_env.py
+++ b/tests/test_flatland_envs_rail_env.py
@@ -28,7 +28,7 @@ def test_load_env():
def test_save_load():
env = RailEnv(width=10, height=10,
- rail_generator=complex_rail_generator(nr_start_goal=2, nr_extra=5, min_dist=6, seed=0),
+ rail_generator=complex_rail_generator(nr_start_goal=2, nr_extra=5, min_dist=6, seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=2)
env.reset()
diff --git a/tests/test_flatland_malfunction.py b/tests/test_flatland_malfunction.py
index 73c831a426d55abf45217d66cba57481f6cc63ea..fe41c2b1461fcb86554ee7b1cefbd34ce408e8dd 100644
--- a/tests/test_flatland_malfunction.py
+++ b/tests/test_flatland_malfunction.py
@@ -170,7 +170,7 @@ def test_malfunction_before_entry():
"""Tests that malfunctions are produced by stochastic_data!"""
# Set fixed malfunction duration for this test
stochastic_data = {'prop_malfunction': 1.,
- 'malfunction_rate': 2,
+ 'malfunction_rate': 1,
'min_duration': 10,
'max_duration': 10}
@@ -187,15 +187,17 @@ def test_malfunction_before_entry():
# reset to initialize agents_static
env.reset(False, False, False, random_seed=10)
env.agents[0].target = (0, 0)
+
assert env.agents[1].malfunction_data['malfunction'] == 11
assert env.agents[2].malfunction_data['malfunction'] == 11
assert env.agents[3].malfunction_data['malfunction'] == 11
assert env.agents[4].malfunction_data['malfunction'] == 11
- assert env.agents[5].malfunction_data['malfunction'] == 0
+ assert env.agents[5].malfunction_data['malfunction'] == 11
assert env.agents[6].malfunction_data['malfunction'] == 11
assert env.agents[7].malfunction_data['malfunction'] == 11
assert env.agents[8].malfunction_data['malfunction'] == 11
- assert env.agents[9].malfunction_data['malfunction'] == 0
+ assert env.agents[9].malfunction_data['malfunction'] == 11
+
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}
@@ -206,16 +208,16 @@ def test_malfunction_before_entry():
action_dict[agent.handle] = RailEnvActions(0)
env.step(action_dict)
+
assert env.agents[1].malfunction_data['malfunction'] == 1
assert env.agents[2].malfunction_data['malfunction'] == 1
assert env.agents[3].malfunction_data['malfunction'] == 1
assert env.agents[4].malfunction_data['malfunction'] == 1
- assert env.agents[5].malfunction_data['malfunction'] == 2
+ assert env.agents[5].malfunction_data['malfunction'] == 1
assert env.agents[6].malfunction_data['malfunction'] == 1
assert env.agents[7].malfunction_data['malfunction'] == 1
assert env.agents[8].malfunction_data['malfunction'] == 1
- assert env.agents[9].malfunction_data['malfunction'] == 3
-
+ assert env.agents[9].malfunction_data['malfunction'] == 1
# Print for test generation
# for a in range(env.get_num_agents()):
# print("assert env.agents[{}].malfunction_data['malfunction'] == {}".format(a,
@@ -226,7 +228,7 @@ def test_malfunction_before_entry():
def test_initial_malfunction():
stochastic_data = {'prop_malfunction': 1., # Percentage of defective agents
- 'malfunction_rate': 70, # Rate of malfunction occurence
+ 'malfunction_rate': 100, # Rate of malfunction occurence
'min_duration': 2, # Minimal duration of malfunction
'max_duration': 5 # Max duration of malfunction
}
@@ -236,7 +238,7 @@ def test_initial_malfunction():
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
- schedule_generator=random_schedule_generator(),
+ schedule_generator=random_schedule_generator(seed=10),
number_of_agents=1,
stochastic_data=stochastic_data, # Malfunction data generator
obs_builder_object=SingleAgentNavigationObs()
@@ -244,6 +246,7 @@ def test_initial_malfunction():
# reset to initialize agents_static
env.reset(False, False, True, random_seed=10)
+ print(env.agents[0].malfunction_data)
env.agents[0].target = (0, 5)
set_penalties_for_replay(env)
replay_config = ReplayConfig(
diff --git a/tests/test_multi_speed.py b/tests/test_multi_speed.py
index 3cd0a4c1d9812c728089255eb1150111b783a463..d190a5769415c65948b2d0e12c7f9b86b044a494 100644
--- a/tests/test_multi_speed.py
+++ b/tests/test_multi_speed.py
@@ -1,4 +1,5 @@
import numpy as np
+from test_utils import ReplayConfig, Replay, run_replay_config, set_penalties_for_replay
from flatland.core.grid.grid4 import Grid4TransitionsEnum
from flatland.envs.observations import TreeObsForRailEnv
@@ -7,7 +8,6 @@ from flatland.envs.rail_env import RailEnv, RailEnvActions
from flatland.envs.rail_generators import complex_rail_generator, rail_from_grid_transition_map
from flatland.envs.schedule_generators import complex_schedule_generator, random_schedule_generator
from flatland.utils.simple_rail import make_simple_rail
-from test_utils import ReplayConfig, Replay, run_replay_config, set_penalties_for_replay
np.random.seed(1)
@@ -52,7 +52,7 @@ def test_multi_speed_init():
env = RailEnv(width=50,
height=50,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999,
- seed=0),
+ seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=5)
# Initialize the agent with the parameters corresponding to the environment and observation_builder
diff --git a/tests/test_random_seeding.py b/tests/test_random_seeding.py
index 3a2fe8bc1775653e15842ac27c262aab3e2b9ded..3a03de00c27795ea4b4a3915a23fec3a48732bbd 100644
--- a/tests/test_random_seeding.py
+++ b/tests/test_random_seeding.py
@@ -1,3 +1,7 @@
+import numpy as np
+
+from flatland.envs.observations import GlobalObsForRailEnv, TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators import rail_from_grid_transition_map
from flatland.envs.schedule_generators import random_schedule_generator
@@ -32,4 +36,136 @@ def test_random_seeding():
# Test generation print
assert env.agents[0].position == (3, 6)
# print("env.agents[0].initial_position == {}".format(env.agents[0].initial_position))
- #print("assert env.agents[0].position == {}".format(env.agents[0].position))
+ # print("assert env.agents[0].position == {}".format(env.agents[0].position))
+
+
+def test_seeding_and_observations():
+ # Test if two different instances diverge with different observations
+ rail, rail_map = make_simple_rail2()
+
+ # Make two seperate envs with different observation builders
+ # Global Observation
+ env = RailEnv(width=25,
+ height=30,
+ rail_generator=rail_from_grid_transition_map(rail),
+ schedule_generator=random_schedule_generator(seed=12),
+ number_of_agents=10,
+ obs_builder_object=GlobalObsForRailEnv()
+ )
+ # Tree Observation
+ env2 = RailEnv(width=25,
+ height=30,
+ rail_generator=rail_from_grid_transition_map(rail),
+ schedule_generator=random_schedule_generator(seed=12),
+ number_of_agents=10,
+ obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
+ )
+
+ env.reset(False, False, False, random_seed=12)
+ env2.reset(False, False, False, random_seed=12)
+
+ # Check that both environments produce the same initial start positions
+ assert env.agents[0].initial_position == env2.agents[0].initial_position
+ assert env.agents[1].initial_position == env2.agents[1].initial_position
+ assert env.agents[2].initial_position == env2.agents[2].initial_position
+ assert env.agents[3].initial_position == env2.agents[3].initial_position
+ assert env.agents[4].initial_position == env2.agents[4].initial_position
+ assert env.agents[5].initial_position == env2.agents[5].initial_position
+ assert env.agents[6].initial_position == env2.agents[6].initial_position
+ assert env.agents[7].initial_position == env2.agents[7].initial_position
+ assert env.agents[8].initial_position == env2.agents[8].initial_position
+ assert env.agents[9].initial_position == env2.agents[9].initial_position
+
+ action_dict = {}
+ for step in range(10):
+ for a in range(env.get_num_agents()):
+ action = np.random.randint(4)
+ action_dict[a] = action
+ env.step(action_dict)
+ env2.step(action_dict)
+
+ # Check that both environments end up in the same position
+
+ assert env.agents[0].position == env2.agents[0].position
+ assert env.agents[1].position == env2.agents[1].position
+ assert env.agents[2].position == env2.agents[2].position
+ assert env.agents[3].position == env2.agents[3].position
+ assert env.agents[4].position == env2.agents[4].position
+ assert env.agents[5].position == env2.agents[5].position
+ assert env.agents[6].position == env2.agents[6].position
+ assert env.agents[7].position == env2.agents[7].position
+ assert env.agents[8].position == env2.agents[8].position
+ assert env.agents[9].position == env2.agents[9].position
+ for a in range(env.get_num_agents()):
+ print("assert env.agents[{}].position == env2.agents[{}].position".format(a, a))
+
+
+def test_seeding_and_malfunction():
+ # Test if two different instances diverge with different observations
+ rail, rail_map = make_simple_rail2()
+
+ stochastic_data = {'prop_malfunction': 1.,
+ 'malfunction_rate': 2,
+ 'min_duration': 10,
+ 'max_duration': 10}
+ # Make two seperate envs with different and see if the exhibit the same malfunctions
+ # Global Observation
+ for tests in range(1, 100):
+ env = RailEnv(width=25,
+ height=30,
+ rail_generator=rail_from_grid_transition_map(rail),
+ schedule_generator=random_schedule_generator(),
+ number_of_agents=10,
+ obs_builder_object=GlobalObsForRailEnv(),
+ stochastic_data=stochastic_data, # Malfunction data generator
+ )
+
+ # Tree Observation
+ env2 = RailEnv(width=25,
+ height=30,
+ rail_generator=rail_from_grid_transition_map(rail),
+ schedule_generator=random_schedule_generator(),
+ number_of_agents=10,
+ obs_builder_object=GlobalObsForRailEnv(),
+ stochastic_data=stochastic_data, # Malfunction data generator
+ )
+
+ env.reset(True, False, True, random_seed=tests)
+ env2.reset(True, False, True, random_seed=tests)
+
+ # Check that both environments produce the same initial start positions
+ assert env.agents[0].initial_position == env2.agents[0].initial_position
+ assert env.agents[1].initial_position == env2.agents[1].initial_position
+ assert env.agents[2].initial_position == env2.agents[2].initial_position
+ assert env.agents[3].initial_position == env2.agents[3].initial_position
+ assert env.agents[4].initial_position == env2.agents[4].initial_position
+ assert env.agents[5].initial_position == env2.agents[5].initial_position
+ assert env.agents[6].initial_position == env2.agents[6].initial_position
+ assert env.agents[7].initial_position == env2.agents[7].initial_position
+ assert env.agents[8].initial_position == env2.agents[8].initial_position
+ assert env.agents[9].initial_position == env2.agents[9].initial_position
+
+ action_dict = {}
+ for step in range(10):
+ for a in range(env.get_num_agents()):
+ action = np.random.randint(4)
+ action_dict[a] = action
+ # print("----------------------")
+ # print(env.agents[a].malfunction_data, env.agents[a].status)
+ # print(env2.agents[a].malfunction_data, env2.agents[a].status)
+
+ env.step(action_dict)
+ env2.step(action_dict)
+
+ # Check that both environments end up in the same position
+
+ assert env.agents[0].position == env2.agents[0].position
+ assert env.agents[1].position == env2.agents[1].position
+ assert env.agents[2].position == env2.agents[2].position
+ assert env.agents[3].position == env2.agents[3].position
+ assert env.agents[4].position == env2.agents[4].position
+ assert env.agents[5].position == env2.agents[5].position
+ assert env.agents[6].position == env2.agents[6].position
+ assert env.agents[7].position == env2.agents[7].position
+ assert env.agents[8].position == env2.agents[8].position
+ assert env.agents[9].position == env2.agents[9].position
diff --git a/tests/test_speed_classes.py b/tests/test_speed_classes.py
index ff5ee56a308ce19559d079b716bde90ad65baf11..1fcf3b3ef0b7cc176d345fa547e91eeeef0a05bd 100644
--- a/tests/test_speed_classes.py
+++ b/tests/test_speed_classes.py
@@ -21,7 +21,7 @@ def test_rail_env_speed_intializer():
env = RailEnv(width=50,
height=50,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999,
- seed=0),
+ seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=10)
env.reset()