diff --git a/agent/async_agent.py b/agent/async_agent.py
index 7dc26b0..cd079f6 100644
--- a/agent/async_agent.py
+++ b/agent/async_agent.py
@@ -65,6 +65,7 @@ class AsyncAgent:
         self.config.steps_lock = mp.Lock()
         self.config.network_lock = mp.Lock()
         self.config.total_steps = mp.Value('i', 0)
+        self.config.total_epochs = mp.Value('i', 0)
         self.config.stop_signal = mp.Value('i', False)
 
     def run(self):
diff --git a/async_worker/ppo.py b/async_worker/ppo.py
index 02af70a..24c003b 100644
--- a/async_worker/ppo.py
+++ b/async_worker/ppo.py
@@ -138,8 +138,9 @@ class ProximalPolicyOptimization:
             obj = ratio * advantages
             obj_clipped = ratio.clamp(1.0 - self.config.ppo_ratio_clip, 1.0 + self.config.ppo_ratio_clip) * advantages
             policy_loss = -torch.min(obj, obj_clipped).mean(0)
+            entropy = actor_net.entropy(std)
             if config.entropy_weight:
-                policy_loss += -config.entropy_weight * actor_net.entropy(std)
+                policy_loss += -config.entropy_weight * entropy
 
             v = critic_net.predict(states)
             value_loss = 0.5 * (returns - v).pow(2).mean()
@@ -148,7 +149,15 @@ class ProximalPolicyOptimization:
             self.worker_network.zero_grad()
             policy_loss.backward()
             value_loss.backward()
-            nn.utils.clip_grad_norm(self.worker_network.parameters(), config.gradient_clip)
+            grad_norm = nn.utils.clip_grad_norm(self.worker_network.parameters(), config.gradient_clip)
+
+            config.logger.scalar_summary('loss_policy', policy_loss, config.total_epochs.value)
+            config.logger.scalar_summary('loss_value', value_loss, config.total_epochs.value)
+            config.logger.scalar_summary('entropy', entropy, config.total_epochs.value)
+            config.logger.histo_summary('ppo_ratio', ratio, config.total_epochs.value)
+            config.logger.histo_summary('advantages', advantages, config.total_epochs.value)
+            config.logger.histo_summary('grad_norm', grad_norm, config.total_epochs.value)
+
             with config.network_lock:
                 self.shared_network.zero_grad()
                 self.actor_opt.zero_grad()
@@ -156,5 +165,6 @@ class ProximalPolicyOptimization:
                 sync_grad(self.shared_network, self.worker_network)
                 self.actor_opt.step()
                 self.critic_opt.step()
+                config.total_epochs.value += 1
 
         return batched_steps, batched_rewards