diff --git a/rl_games/algos_torch/players.py b/rl_games/algos_torch/players.py
index 3dfcdadd..69df7913 100644
--- a/rl_games/algos_torch/players.py
+++ b/rl_games/algos_torch/players.py
@@ -199,7 +199,7 @@ def __init__(self, params):
         ]
 
         obs_shape = self.obs_shape
-        self.normalize_input = False
+        self.normalize_input = self.config.get('normalize_input', False)
         config = {
             'obs_dim': self.env_info["observation_space"].shape[0],
             'action_dim': self.env_info["action_space"].shape[0],
@@ -229,8 +229,9 @@ def restore(self, fn):
     def get_action(self, obs, is_deterministic=False):
         if self.has_batch_dimension == False:
             obs = unsqueeze_obs(obs)
+        obs = self.model.norm_obs(obs)
         dist = self.model.actor(obs)
-        actions = dist.sample() if is_deterministic else dist.mean
+        actions = dist.sample() if not is_deterministic else dist.mean
         actions = actions.clamp(*self.action_range).to(self.device)
         if self.has_batch_dimension == False:
             actions = torch.squeeze(actions.detach())
diff --git a/rl_games/algos_torch/sac_agent.py b/rl_games/algos_torch/sac_agent.py
index 4dc31a51..909ae3e5 100644
--- a/rl_games/algos_torch/sac_agent.py
+++ b/rl_games/algos_torch/sac_agent.py
@@ -211,6 +211,8 @@ def get_weights(self):
         state = {'actor': self.model.sac_network.actor.state_dict(),
          'critic': self.model.sac_network.critic.state_dict(), 
          'critic_target': self.model.sac_network.critic_target.state_dict()}
+        if self.normalize_input:
+            state['running_mean_std'] = self.model.running_mean_std.state_dict()
         return state
 
     def save(self, fn):
diff --git a/rl_games/common/a2c_common.py b/rl_games/common/a2c_common.py
index 7263e1f1..bda3d38f 100644
--- a/rl_games/common/a2c_common.py
+++ b/rl_games/common/a2c_common.py
@@ -1320,6 +1320,7 @@ def train(self):
         self.curr_frames = self.batch_size_envs
 
         if self.multi_gpu:
+            torch.cuda.set_device(self.local_rank)
             print("====================broadcasting parameters")
             model_params = [self.model.state_dict()]
             dist.broadcast_object_list(model_params, 0)