remove verbosity from benchmark

benchmarks/play_model_benchmark.py

 import random
-import time
-from collections import deque
 
 import numpy as np
 from benchmarker import Benchmarker
 
 from flatland.envs.generators import complex_rail_generator
 from flatland.envs.rail_env import RailEnv
-from flatland.utils.rendertools import RenderTool
 
 
-def main(render=True, delay=0.0):
+def main():
     random.seed(1)
     np.random.seed(1)
 
@@ -19,18 +16,8 @@ def main(render=True, delay=0.0):
                   rail_generator=complex_rail_generator(nr_start_goal=5, nr_extra=20, min_dist=12),
                   number_of_agents=5)
 
-    if render:
-        env_renderer = RenderTool(env, gl="QTSVG")
-
     n_trials = 20
-    eps = 1.
-    eps_end = 0.005
-    eps_decay = 0.998
     action_dict = dict()
-    scores_window = deque(maxlen=100)
-    done_window = deque(maxlen=100)
-    scores = []
-    dones_list = []
     action_prob = [0] * 4
 
     def max_lt(seq, val):
@@ -46,82 +33,32 @@ def main(render=True, delay=0.0):
             idx -= 1
         return None
 
-    iFrame = 0
-    tStart = time.time()
     for trials in range(1, n_trials + 1):
 
         # Reset environment
         obs = env.reset()
-        if render:
-            env_renderer.set_new_rail()
 
         for a in range(env.get_num_agents()):
             norm = max(1, max_lt(obs[a], np.inf))
             obs[a] = np.clip(np.array(obs[a]) / norm, -1, 1)
 
-        # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
-
-        score = 0
-        env_done = 0
-
         # Run episode
         for step in range(100):
-            # if trials > 114:
-            # env_renderer.renderEnv(show=True)
-            # print(step)
             # Action
             for a in range(env.get_num_agents()):
                 action = np.random.randint(0, 4)
                 action_prob[action] += 1
                 action_dict.update({a: action})
 
-            if render:
-                env_renderer.renderEnv(show=True, frames=True, iEpisode=trials, iStep=step, action_dict=action_dict)
-                if delay > 0:
-                    time.sleep(delay)
-
-            iFrame += 1
-
             # Environment step
             next_obs, all_rewards, done, _ = env.step(action_dict)
             for a in range(env.get_num_agents()):
                 norm = max(1, max_lt(next_obs[a], np.inf))
                 next_obs[a] = np.clip(np.array(next_obs[a]) / norm, -1, 1)
-            # Update replay buffer and train agent
-            for a in range(env.get_num_agents()):
-                # agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
-                score += all_rewards[a]
 
-            obs = next_obs.copy()
             if done['__all__']:
-                env_done = 1
                 break
-        # Epsilon decay
-        eps = max(eps_end, eps_decay * eps)  # decrease epsilon
-
-        done_window.append(env_done)
-        scores_window.append(score)  # save most recent score
-        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.get_num_agents(),
-            trials,
-            np.mean(scores_window),
-            100 * np.mean(done_window),
-            eps, action_prob / np.sum(action_prob)),
-            end=" ")
         if trials % 100 == 0:
-            tNow = time.time()
-            rFps = iFrame / (tNow - tStart)
-            print(('\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%' +
-                   '\tEpsilon: {:.2f} fps: {:.2f} \t Action Probabilities: \t {}').format(
-                env.get_num_agents(),
-                trials,
-                np.mean(scores_window),
-                100 * np.mean(done_window),
-                eps, rFps, action_prob / np.sum(action_prob)))
             action_prob = [1] * 4
 
 
@@ -129,4 +66,4 @@ if __name__ == "__main__":
     with Benchmarker(cycle=20, extra=1) as bench:
         @bench("Everything")
         def _(bm):
-            main(render=False, delay=0)
+            main()

setup.py

@@ -21,13 +21,12 @@ with open('HISTORY.rst') as history_file:
 
 
 
-# install pycairo
+# install pycairo on Windows
 if os.name == 'nt':
     p = platform.architecture()
     is64bit = p[0] == '64bit'
     if sys.version[0:3] == '3.5':
         if is64bit:
-
             url = 'https://download.lfd.uci.edu/pythonlibs/t4jqbe6o/pycairo-1.18.0-cp35-cp35m-win_amd64.whl'
         else:
             url = 'https://download.lfd.uci.edu/pythonlibs/t4jqbe6o/pycairo-1.18.0-cp35-cp35m-win32.whl'
@@ -47,7 +46,7 @@ if os.name == 'nt':
     try:
         import pycairo
     except:
-        call_cmd = "pip install " + url
+        call_cmd = "pip install --user " + url
         os.system(call_cmd)
 
         import site