diff --git a/tests/test_flatland_malfunction.py b/tests/test_flatland_malfunction.py
index ce6e1283fbb1155f81aa722a20829c732042e282..a46467ed4cbefdf38ea4ce8b0be4defadc2cab1e 100644
--- a/tests/test_flatland_malfunction.py
+++ b/tests/test_flatland_malfunction.py
@@ -156,14 +156,20 @@ def test_malfunction_process_statistically():
env.agents[0].target = (0, 0)
# Next line only for test generation
- #agent_malfunction_list = [[] for i in range(20)]
- agent_malfunction_list = [[0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 0, 0, 0, 0],
- [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 0, 0, 5], [0, 0, 0, 0, 1, 1, 5, 0, 0, 4],
- [0, 0, 0, 5, 0, 0, 4, 5, 0, 3], [5, 0, 0, 4, 0, 0, 3, 4, 0, 2], [4, 5, 0, 3, 5, 5, 2, 3, 5, 1],
- [3, 4, 0, 2, 4, 4, 1, 2, 4, 0], [2, 3, 5, 1, 3, 3, 0, 1, 3, 0], [1, 2, 4, 0, 2, 2, 0, 0, 2, 0],
- [0, 1, 3, 0, 1, 1, 5, 0, 1, 0], [0, 0, 2, 0, 0, 0, 4, 0, 0, 0], [5, 0, 1, 0, 0, 0, 3, 5, 0, 5],
- [4, 0, 0, 0, 5, 0, 2, 4, 0, 4], [3, 0, 0, 0, 4, 0, 1, 3, 5, 3], [2, 0, 0, 0, 3, 0, 0, 2, 4, 2],
- [1, 0, 5, 5, 2, 0, 0, 1, 3, 1], [0, 5, 4, 4, 1, 0, 5, 0, 2, 0]]
+ # agent_malfunction_list = [[] for i in range(20)]
+ agent_malfunction_list = [[0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],
+ [0, 0, 0, 0, 4, 4, 0, 0, 0, 0],
+ [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 0, 0, 5],
+ [0, 0, 0, 0, 1, 1, 5, 0, 0, 4],
+ [0, 0, 0, 5, 0, 0, 4, 5, 0, 3], [5, 0, 0, 4, 0, 0, 3, 4, 0, 2],
+ [4, 5, 0, 3, 5, 5, 2, 3, 5, 1],
+ [3, 4, 0, 2, 4, 4, 1, 2, 4, 0], [2, 3, 5, 1, 3, 3, 0, 1, 3, 0],
+ [1, 2, 4, 0, 2, 2, 0, 0, 2, 0],
+ [0, 1, 3, 0, 1, 1, 5, 0, 1, 0], [0, 0, 2, 0, 0, 0, 4, 0, 0, 0],
+ [5, 0, 1, 0, 0, 0, 3, 5, 0, 5],
+ [4, 0, 0, 0, 5, 0, 2, 4, 0, 4], [3, 0, 0, 0, 4, 0, 1, 3, 5, 3],
+ [2, 0, 0, 0, 3, 0, 0, 2, 4, 2],
+ [1, 0, 5, 5, 2, 0, 0, 1, 3, 1], [0, 5, 4, 4, 1, 0, 5, 0, 2, 0]]
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}
@@ -175,7 +181,7 @@ def test_malfunction_process_statistically():
assert env.agents[agent_idx].malfunction_data['malfunction'] == agent_malfunction_list[step][agent_idx]
env.step(action_dict)
# For generating test onlz
- #print(agent_malfunction_list)
+ # print(agent_malfunction_list)
def test_malfunction_before_entry():
@@ -213,6 +219,7 @@ def test_malfunction_before_entry():
assert env.agents[8].malfunction_data['malfunction'] == 0
assert env.agents[9].malfunction_data['malfunction'] == 0
+
def test_next_malfunction_counter():
"""
Test that the next malfunction occurs when desired
@@ -237,7 +244,7 @@ def test_next_malfunction_counter():
env.agents[0].malfunction_data['next_malfunction'] = 5
env.agents[0].malfunction_data['malfunction_rate'] = 5
env.agents[0].malfunction_data['malfunction'] = 0
- env.agents[0].target =(0, 0), #Move the target out of range
+ env.agents[0].target = (0, 0), # Move the target out of range
print(env.agents[0].position, env.agents[0].malfunction_data['next_malfunction'])
for time_step in range(1, 6):
@@ -248,6 +255,47 @@ def test_next_malfunction_counter():
assert env.agents[0].malfunction_data['next_malfunction'] == 5 - time_step
+def test_malfunction_values_and_behavior():
+ """
+ Test that the next malfunction occurs when desired.
+ Returns
+ -------
+
+ """
+ # Set fixed malfunction duration for this test
+
+ rail, rail_map = make_simple_rail2()
+ action_dict: Dict[int, RailEnvActions] = {}
+ stochastic_data = {'prop_malfunction': 1.,
+ 'malfunction_rate': 5,
+ 'min_duration': 10,
+ 'max_duration': 10}
+ env = RailEnv(width=25,
+ height=30,
+ rail_generator=rail_from_grid_transition_map(rail),
+ schedule_generator=random_schedule_generator(seed=2), # seed 12
+ stochastic_data=stochastic_data,
+ number_of_agents=1,
+ random_seed=1,
+ )
+ # reset to initialize agents_static
+ env.reset(False, False, activate_agents=True, random_seed=10)
+ env.agents[0].malfunction_data['next_malfunction'] = 5
+ env.agents[0].malfunction_data['malfunction_rate'] = 50
+ env.agents[0].malfunction_data['malfunction'] = 0
+ env.agents[0].target = (0, 0), # Move the target out of range
+ print(env.agents[0].position, env.agents[0].malfunction_data['next_malfunction'])
+
+ for time_step in range(1, 16):
+ # Move in the env
+ env.step(action_dict)
+ print(time_step)
+ # Check that next_step decreases as expected
+ if env.agents[0].malfunction_data['malfunction'] < 1:
+ assert env.agents[0].malfunction_data['next_malfunction'] == np.clip(5 - time_step, 0, 100)
+ else:
+ assert env.agents[0].malfunction_data['malfunction'] == np.clip(10 - (time_step - 6), 0, 100)
+
def test_initial_malfunction():
stochastic_data = {'prop_malfunction': 1., # Percentage of defective agents
@@ -295,15 +343,15 @@ def test_initial_malfunction():
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=1,
- reward= env.step_penalty * 1.0
+ reward=env.step_penalty * 1.0
- ),# malfunctioning ends: starting and running at speed 1.0
+ ), # malfunctioning ends: starting and running at speed 1.0
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
- reward=env.start_penalty +env.step_penalty * 1.0 # running at speed 1.0
+ reward=env.start_penalty + env.step_penalty * 1.0 # running at speed 1.0
),
Replay(
position=(3, 3),