updated tests to new exponential distirbution random generator

flatland/envs/rail_env.py

@@ -360,10 +360,8 @@ class RailEnv(Environment):
 
             # Next malfunction in number of stops
             next_breakdown = int(
-                self.np_random.exponential(scale=agent.malfunction_data['malfunction_rate']))
-            next_breakdown = self.np_random.randint(self.min_number_of_steps_broken,
-                                                    self.max_number_of_steps_broken + 1) + 1
-            agent.malfunction_data['next_malfunction'] = 5  # next_breakdown
+                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,

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}
 
@@ -187,9 +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)
-    for a in range(env.get_num_agents()):
-        print("assert env.agents[{}].malfunction_data['malfunction'] == {}".format(a, env.agents[a].malfunction_data[
-            'malfunction']))
+
+    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'] == 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'] == 11
+
 
     for step in range(20):
         action_dict: Dict[int, RailEnvActions] = {}
@@ -200,16 +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,
@@ -220,7 +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
                        }
@@ -230,7 +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()
@@ -238,6 +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(

tests/test_random_seeding.py

@@ -168,3 +168,23 @@ def test_seeding_and_malfunction():
     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 tests_new_distributio():
+    def _exp_distirbution_synced(rate):
+        """
+        Generates sample from exponential distribution
+        We need this to guarantee synchronity between different instances with same seed.
+        :param rate:
+        :return:
+        """
+        u = np.random.rand()
+        x = - np.log(1 - u) * rate
+        return x
+
+    numbers = []
+    for i in range(100):
+        rate1 = 2
+        rate2 = 100
+        print((_exp_distirbution_synced(rate1), _exp_distirbution_synced(rate2)))
+    print(numbers)