reformatting file

examples/training_navigation.py

@@ -20,8 +20,8 @@ transition_probability = [5,  # empty cell - Case 0
                           0]  # Case 7 - dead end
 
 # Example generate a random rail
-env = RailEnv(width=10,
-              height=10,
+env = RailEnv(width=15,
+              height=15,
               rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
               number_of_agents=3)
 env_renderer = RenderTool(env, gl="QT")
@@ -38,37 +38,41 @@ scores_window = deque(maxlen=100)
 done_window = deque(maxlen=100)
 scores = []
 dones_list = []
-action_prob = [0]*4
+action_prob = [0] * 4
 agent = Agent(state_size, action_size, "FC", 0)
-agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint15000.pth'))
+agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint13900.pth'))
+
+demo = True
+
 
-demo = False
 def max_lt(seq, val):
     """
     Return greatest item in seq for which item < val applies.
     None is returned if seq was empty or all items in seq were >= val.
     """
     max = 0
-    idx = len(seq)-1
+    idx = len(seq) - 1
     while idx >= 0:
         if seq[idx] < val and seq[idx] >= 0 and seq[idx] > max:
             max = seq[idx]
         idx -= 1
     return max
 
+
 def min_lt(seq, val):
     """
     Return smallest item in seq for which item > val applies.
     None is returned if seq was empty or all items in seq were >= val.
     """
     min = np.inf
-    idx = len(seq)-1
+    idx = len(seq) - 1
     while idx >= 0:
         if seq[idx] > val and seq[idx] < min:
             min = seq[idx]
         idx -= 1
     return min
 
+
 for trials in range(1, n_trials + 1):
 
     # Reset environment
@@ -86,7 +90,7 @@ for trials in range(1, n_trials + 1):
     for step in range(100):
         if demo:
             env_renderer.renderEnv(show=True)
-        #print(step)
+        # print(step)
         # Action
         for a in range(env.number_of_agents):
             if demo:
@@ -117,17 +121,17 @@ for trials in range(1, n_trials + 1):
     scores.append(np.mean(scores_window))
     dones_list.append((np.mean(done_window)))
 
-    print('\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
-        env.number_of_agents,
-        trials,
-        np.mean(
-            scores_window),
-        100 * np.mean(
-            done_window),
-        eps, action_prob/np.sum(action_prob)),
-          end=" ")
+    print(
+        '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
+            env.number_of_agents,
+            trials,
+            np.mean(
+                scores_window),
+            100 * np.mean(
+                done_window),
+            eps, action_prob / np.sum(action_prob)),
+        end=" ")
     if trials % 100 == 0:
-
         print(
             '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
                 env.number_of_agents,
@@ -139,4 +143,4 @@ for trials in range(1, n_trials + 1):
                 eps, action_prob / np.sum(action_prob)))
         torch.save(agent.qnetwork_local.state_dict(),
                    '../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
-        action_prob = [1]*4
+        action_prob = [1] * 4