#92 reward function test

torch_training/bla.py

@@ -92,24 +92,24 @@ def main(argv):
     print("Going to run training for {} trials...".format(n_trials))
     for trials in range(1, n_trials + 1):
 
-        # if trials % 50 == 0 and not demo:
-        #     x_dim = np.random.randint(8, 20)
-        #     y_dim = np.random.randint(8, 20)
-        #     n_agents = np.random.randint(3, 8)
-        #     n_goals = n_agents + np.random.randint(0, 3)
-        #     min_dist = int(0.75 * min(x_dim, y_dim))
-        #     env = RailEnv(width=x_dim,
-        #                   height=y_dim,
-        #                   rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
-        #                                                         max_dist=99999,
-        #                                                         seed=0),
-        #                   obs_builder_object=TreeObsForRailEnv(max_depth=3,
-        #                                                        predictor=ShortestPathPredictorForRailEnv()),
-        #                   number_of_agents=n_agents)
-        #     env.reset(True, True)
-        #     max_steps = int(3 * (env.height + env.width))
-        #     agent_obs = [None] * env.get_num_agents()
-        #     agent_next_obs = [None] * env.get_num_agents()
+        if trials % 50 == 0 and not demo:
+            x_dim = np.random.randint(8, 20)
+            y_dim = np.random.randint(8, 20)
+            n_agents = np.random.randint(3, 8)
+            n_goals = n_agents + np.random.randint(0, 3)
+            min_dist = int(0.75 * min(x_dim, y_dim))
+            env = RailEnv(width=x_dim,
+                          height=y_dim,
+                          rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
+                                                                max_dist=99999,
+                                                                seed=0),
+                          obs_builder_object=TreeObsForRailEnv(max_depth=3,
+                                                               predictor=ShortestPathPredictorForRailEnv()),
+                          number_of_agents=n_agents)
+            env.reset(True, True)
+            max_steps = int(3 * (env.height + env.width))
+            agent_obs = [None] * env.get_num_agents()
+            agent_next_obs = [None] * env.get_num_agents()
         # # Reset environment
         # if file_load:
         #     obs = env.reset(False, False)