Add linting to travis

.pylintrc

@@ -126,7 +126,8 @@ disable=print-statement,
         next-method-defined,
         dict-items-not-iterating,
         dict-keys-not-iterating,
-        dict-values-not-iterating
+        dict-values-not-iterating,
+        missing-docstring # @TODO: fix later
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

.travis.yml

@@ -2,7 +2,11 @@ language: python
 python:
   - "3.6"
 
+before_script:
+  - pip install -r requirements_dev.txt
+
 script:
+  - pylint torchrl/ examples/
   - pip install .
 
 deploy:

VERSION

@@ -1 +1 @@
-0.3.0
+0.3.1

examples/a2c_cartpole.py

@@ -8,11 +8,11 @@
 from torchrl.archs import SimpleQNet
 from torchrl.algorithms import A2C
 
-viz = visdom.Visdom()
-window = None
-
 
 def main():
+    viz = visdom.Visdom()
+    window = None
+
     env = gym.make('CartPole-v1')
     qnet = SimpleQNet(env.observation_space.shape[0], env.action_space.n)
     agent = DQN(qnet)
@@ -28,7 +28,6 @@ def main():
 
     # @TODO A general method to collect training stats?
     if viz.check_connection():
-        global window
         window = viz.line(torch.FloatTensor(reward_list), torch.FloatTensor(list(range(1, num_episodes + 1))),
                           win=window, name='training_episode_rewards', update='replace' if window else None,
                           opts={'title': 'Training Rewards', 'xlabel': 'Episode',

examples/dqn_cartpole.py

@@ -8,11 +8,11 @@
 from torchrl.archs import SimpleQNet
 from torchrl.algorithms import QLearning
 
-viz = visdom.Visdom()
-window = None
-
 
 def main():
+    viz = visdom.Visdom()
+    window = None
+
     env = gym.make('CartPole-v1')
     qnet = SimpleQNet(env.observation_space.shape[0], env.action_space.n)
     agent = DQN(qnet)
@@ -29,7 +29,6 @@ def main():
 
     # @TODO A general method to collect training stats?
     if viz.check_connection():
-        global window
         window = viz.line(torch.FloatTensor(reward_list), torch.FloatTensor(list(range(1, num_episodes + 1))),
                           win=window, name='training_episode_rewards', update='replace' if window else None,
                           opts={'title': 'Training Rewards', 'xlabel': 'Episode',