diff --git a/torchy_baselines/common/base_class.py b/torchy_baselines/common/base_class.py
index 1e78608..f29fc72 100644
--- a/torchy_baselines/common/base_class.py
+++ b/torchy_baselines/common/base_class.py
@@ -91,7 +91,7 @@ class BaseRLModel(ABC):
         raise NotImplementedError()
 
     @abstractmethod
-    def learn(self, total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name="run",
+    def learn(self, total_timesteps, callback=None, log_interval=100, tb_log_name="run",
               reset_num_timesteps=True):
         """
         Return a trained model.
diff --git a/torchy_baselines/td3/td3.py b/torchy_baselines/td3/td3.py
index 5702e87..476fc67 100644
--- a/torchy_baselines/td3/td3.py
+++ b/torchy_baselines/td3/td3.py
@@ -1,3 +1,5 @@
+import sys
+
 import torch as th
 import torch.nn.functional as F
 import numpy as np
@@ -22,7 +24,7 @@ class TD3(BaseRLModel):
 
         super(TD3, self).__init__(policy, env, TD3Policy, policy_kwargs, verbose)
 
-        self.max_action = float(self.action_space.high)
+        self.max_action = np.abs(self.action_space.high)
         self.replay_buffer = None
         self.device = device
         self.action_noise_std = action_noise_std
@@ -38,6 +40,9 @@ class TD3(BaseRLModel):
         state_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0]
         set_random_seed(self.seed, using_cuda=self.device != 'cpu')
 
+        if self.env is not None:
+            self.env.seed(self.seed)
+
         self.replay_buffer = ReplayBuffer(self.buffer_size, state_dim, action_dim, self.device)
         self.policy = self.policy(self.observation_space, self.action_space,
                                   self.learning_rate, device=self.device, **self.policy_kwargs)
@@ -113,7 +118,7 @@ class TD3(BaseRLModel):
                 for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
                     target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
 
-    def learn(self, total_timesteps, callback=None, seed=None, log_interval=100,
+    def learn(self, total_timesteps, callback=None, log_interval=100,
               eval_freq=-1, n_eval_episodes=5, tb_log_name="TD3", reset_num_timesteps=True):
 
         timesteps_since_eval = 0
@@ -141,6 +146,7 @@ class TD3(BaseRLModel):
                     evaluations.append(evaluate_policy(self, self.env, n_eval_episodes))
                     if self.verbose > 0:
                         print("Eval num_timesteps={}, mean_reward={:.2f}".format(self.num_timesteps, evaluations[-1]))
+                        sys.stdout.flush()
 
                 # Reset environment
                 obs = self.env.reset()