diff --git a/main.py b/main.py
index f2059f2..01c5e21 100644
--- a/main.py
+++ b/main.py
@@ -402,7 +402,7 @@ def n_step_dqn_pixel_atari(name):
     config.num_workers = 8
     config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers)
     config.optimizer_fn = lambda params: torch.optim.RMSprop(params, lr=0.00025, alpha=0.95, eps=0.01)
-    config.network_fn = lambda: NatureConvNet(config.history_length, task.action_dim, gpu=1)
+    config.network_fn = lambda: NatureConvNet(config.history_length, task.action_dim, gpu=0)
     config.policy_fn = lambda: GreedyPolicy(epsilon=1.0, final_step=1000000, min_epsilon=0.1)
     config.reward_shift_fn = lambda r: np.sign(r)
     config.discount = 0.99
@@ -423,7 +423,7 @@ if __name__ == '__main__':
     # categorical_dqn_cart_pole()
     # async_cart_pole()
     # a3c_cart_pole()
-    # a2c_cart_pole()
+    a2c_cart_pole()
     # a3c_continuous()
     # p3o_continuous()
     # d3pg_continuous()
@@ -432,7 +432,7 @@ if __name__ == '__main__':
 
     # dqn_pixel_atari('PongNoFrameskip-v4')
     # categorical_dqn_pixel_atari('PongNoFrameskip-v4')
-    n_step_dqn_pixel_atari('PongNoFrameskip-v4')
+    # n_step_dqn_pixel_atari('PongNoFrameskip-v4')
     # async_pixel_atari('PongNoFrameskip-v4')
     # a3c_pixel_atari('PongNoFrameskip-v4')
     # a2c_pixel_atari('PongNoFrameskip-v4')
diff --git a/utils/misc.py b/utils/misc.py
index 04bc96e..8dbdeda 100644
--- a/utils/misc.py
+++ b/utils/misc.py
@@ -58,16 +58,25 @@ def run_episodes(agent):
 
 def run_iterations(agent):
     config = agent.config
-    agent_type = agent.__class__.__name__
+    agent_name = agent.__class__.__name__
     iteration = 0
+    steps = []
+    rewards = []
     while True:
         agent.iteration()
+        steps.append(agent.total_steps)
+        rewards.append(np.mean(agent.last_episode_rewards))
         if iteration % config.iteration_log_interval == 0:
             config.logger.info('total steps %d, mean/max/min reward %f/%f/%f' % (
                 agent.total_steps, np.mean(agent.last_episode_rewards),
                 np.max(agent.last_episode_rewards),
                 np.min(agent.last_episode_rewards)
             ))
+        if iteration % (config.iteration_log_interval * 100) == 0:
+            with open('data/%s-%s-online-stats-%s.bin' % (agent_name, config.tag, agent.task.name), 'wb') as f:
+                pickle.dump({'rewards': rewards,
+                             'steps': steps}, f)
+            agent.save('data/%s-%s-model-%s.bin' % (agent_name, config.tag, agent.task.name))
         iteration += 1
 
 def sync_grad(target_network, src_network):
diff --git a/utils/tf_logger.py b/utils/tf_logger.py
index a608a80..6b532e8 100644
--- a/utils/tf_logger.py
+++ b/utils/tf_logger.py
@@ -13,7 +13,7 @@ class Logger(object):
         try:
             for f in os.listdir(log_dir):
                 os.remove('%s/%s' % (log_dir, f))
-        except FileNotFoundError:
+        except IOError:
             os.mkdir(log_dir)
         if not skip:
             self.writer = SummaryWriter(log_dir)