diff --git a/task.py b/task.py
index 5d3a16e..1eac951 100644
--- a/task.py
+++ b/task.py
@@ -18,25 +18,54 @@ class MountainCar(BasicTask):
     state_space_size = 2
     action_space_size = 3
     name = 'MountainCar-v0'
+    success_threshold = -110
+    discount = 0.99
+    step_limit = 5000
+    target_network_update_freq = 1000
+
     def __init__(self):
         self.env = gym.make(self.name)
         self.env._max_episode_steps = sys.maxsize
 
+        optimizer_fn = lambda name: tf.train.GradientDescentOptimizer(name=name, learning_rate=0.01)
+        self.network_fn = lambda name: Network(name, self.state_space_size,
+                                               self.action_space_size, optimizer_fn, tf.random_normal_initializer())
+        self.policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
+        self.replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
+
+class LunarLander(BasicTask):
+    state_space_size = 8
+    action_space_size = 4
+    name = 'LunarLander-v2'
+    success_threshold = 200
+    discount = 0.99
+    step_limit = 5000
+    target_network_update_freq = 200
+
+    def __init__(self):
+        self.env = gym.make(self.name)
+        optimizer_fn = lambda name: tf.train.GradientDescentOptimizer(name=name, learning_rate=0.001)
+        self.network_fn = lambda name: Network(name, self.state_space_size,
+                                               self.action_space_size, optimizer_fn, tf.random_normal_initializer())
+        self.policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.99, min_epsilon=0)
+        self.replay_fn = lambda: Replay(memory_size=20000, batch_size=100)
+
 if __name__ == '__main__':
-    task = MountainCar()
-    optimizer_fn = lambda name: tf.train.GradientDescentOptimizer(name=name, learning_rate=0.01)
-    network_fn = lambda name: Network(name, task.state_space_size,
-                                      task.action_space_size, optimizer_fn, tf.random_normal_initializer())
-    policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
-    replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
-    agent = DQNAgent('mountain-car', task, network_fn, policy_fn, replay_fn,
-                     discount=0.99, step_limit=5000, target_network_update_freq=1000)
+    # task = MountainCar()
+    task = LunarLander()
+    agent = DQNAgent(task.name, task, task.network_fn, task.policy_fn, task.replay_fn,
+                     task.discount, task.step_limit, task.target_network_update_freq)
+    window_size = 100
     with tf.Session() as sess:
         sess.run(tf.global_variables_initializer())
         ep = 0
+        rewards = []
         while True:
             ep += 1
             reward = agent.episode(sess)
+            rewards.append(reward)
+            if len(rewards) > window_size:
+                reward = np.mean(rewards[-window_size:])
             print 'episode %d: %f' % (ep, reward)
-            if reward > -110:
+            if reward > task.success_threshold:
                 break