Merge pull request #630 from cathywu/visualizer

Minor changes to RLlib visualizer

flow/visualize/visualizer_rllib.py

@@ -101,7 +101,7 @@ def visualizer_rllib(args):
               'python ./visualizer_rllib.py /tmp/ray/result_dir 1 --run PPO')
         sys.exit(1)
 
-    sim_params.restart_instance = False
+    sim_params.restart_instance = True
     dir_path = os.path.dirname(os.path.realpath(__file__))
     emission_path = '{0}/test_time_rollout/'.format(dir_path)
     sim_params.emission_path = emission_path if args.gen_emission else None
@@ -115,6 +115,9 @@ def visualizer_rllib(args):
         sim_params.pxpm = 4
     elif args.render_mode == 'sumo_gui':
         sim_params.render = True
+        print('NOTE: With render mode {}, an extra instance of the SUMO GUI '
+              'will display before the GUI for visualizing the result. Click '
+              'the green Play arrow to continue.'.format(args.render_mode))
     elif args.render_mode == 'no_render':
         sim_params.render = False
     if args.save_render:
@@ -123,8 +126,7 @@ def visualizer_rllib(args):
         sim_params.save_render = True
 
     # Create and register a gym+rllib env
-    create_env, env_name = make_create_env(
-        params=flow_params, version=0)
+    create_env, env_name = make_create_env(params=flow_params, version=0)
     register_env(env_name, create_env)
 
     # check if the environment is a single or multiagent environment, and
@@ -192,8 +194,10 @@ def visualizer_rllib(args):
     env.restart_simulation(
         sim_params=sim_params, render=sim_params.render)
 
+    # Simulate and collect metrics
     final_outflows = []
     mean_speed = []
+    std_speed = []
     for i in range(args.num_rollouts):
         vel = []
         state = env.reset()
@@ -236,23 +240,42 @@ def visualizer_rllib(args):
         outflow = vehicles.get_outflow_rate(500)
         final_outflows.append(outflow)
         mean_speed.append(np.mean(vel))
+        std_speed.append(np.std(vel))
         if multiagent:
             for agent_id, rew in rets.items():
                 print('Round {}, Return: {} for agent {}'.format(
                     i, ret, agent_id))
         else:
             print('Round {}, Return: {}'.format(i, ret))
+
+    print('==== Summary of results ====')
+    print("Return:")
+    print(mean_speed)
     if multiagent:
         for agent_id, rew in rets.items():
+            print('For agent', agent_id)
+            print(rew)
             print('Average, std return: {}, {} for agent {}'.format(
                 np.mean(rew), np.std(rew), agent_id))
     else:
-        print('Average, std return: {}, {}'.format(
+        print(rets)
+        print('Average, std: {}, {}'.format(
             np.mean(rets), np.std(rets)))
-    print('Average, std speed: {}, {}'.format(
-        np.mean(mean_speed), np.std(mean_speed)))
-    print('Average, std outflow: {}, {}'.format(
-        np.mean(final_outflows), np.std(final_outflows)))
+
+    print("\nSpeed, mean (m/s):")
+    print(mean_speed)
+    print('Average, std: {}, {}'.format(np.mean(mean_speed), np.std(
+        mean_speed)))
+    print("\nSpeed, std (m/s):")
+    print(std_speed)
+    print('Average, std: {}, {}'.format(np.mean(std_speed), np.std(
+        std_speed)))
+
+    # Compute arrival rate of vehicles in the last 500 sec of the run
+    print("\nOutflows (veh/hr):")
+    print(final_outflows)
+    print('Average, std: {}, {}'.format(np.mean(final_outflows),
+                                        np.std(final_outflows)))
 
     # terminate the environment
     env.unwrapped.terminate()
@@ -333,7 +356,8 @@ def create_parser():
     parser.add_argument(
         '--save_render',
         action='store_true',
-        help='saves the render to a file')
+        help='Saves a rendered video to a file. NOTE: Overrides render_mode '
+             'with pyglet rendering.')
     parser.add_argument(
         '--horizon',
         type=int,