diff --git a/torchy_baselines/a2c/a2c.py b/torchy_baselines/a2c/a2c.py
index fed4290..8807c91 100644
--- a/torchy_baselines/a2c/a2c.py
+++ b/torchy_baselines/a2c/a2c.py
@@ -64,8 +64,6 @@ class A2C(PPO):
         self.normalize_advantage = normalize_advantage
         self.rms_prop_eps = rms_prop_eps
         self.use_rms_prop = use_rms_prop
-        self.actions = []
-        self.states = []
 
         if _init_setup_model:
             self._setup_model()
@@ -127,38 +125,6 @@ class A2C(PPO):
         logger.logkv("value_loss", value_loss.item())
         logger.logkv("std", th.exp(self.policy.log_std).mean().item())
 
-        self.states.append(self.rollout_buffer.observations.cpu().numpy())
-        self.actions.append(self.rollout_buffer.actions.cpu().numpy())
-
-        # Plot for MountainCarContinuous-v0
-        if True:
-            if len(self.actions) > 10:
-                import matplotlib.pyplot as plt
-                import numpy as np
-                actions = np.concatenate(self.actions)
-                x = np.arange(len(actions))
-                plt.figure("actions")
-                start = 0
-                for i in range(len(self.actions)):
-                    end = start + len(self.actions[i])
-                    # plt.plot(x[start:end], self.actions[i])
-                    # Clipped actions: real behavior, note that it is between [-2, 2] for the Pendulum
-                    plt.scatter(x[start:end], np.clip(self.actions[i], -1, 1), s=1)
-                    # plt.scatter(x[start:end], self.actions[i], s=1)
-                    start = end
-
-                plt.figure("states")
-                for i in range(len(self.states)):
-                    if len(self.states[i].shape) > 1:
-                        # plt.plot(self.states[i][:, 0], self.states[i][:, 1])
-                        plt.scatter(self.states[i][:, 0], self.states[i][:, 1], s=1)
-                    else:
-                        plt.scatter(x[start:end], self.states[i], s=1)
-
-                plt.show()
-                import ipdb; ipdb.set_trace()
-
-
     def learn(self, total_timesteps, callback=None, log_interval=5,
               eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name="A2C", reset_num_timesteps=True):