Fix SAC with input normalization (#291)

* If self.normalize_input is True in SACAgent class, add the weights of the running_mean_std layer in get_weights method

* Allow getting normalize_input from config and use self.model.norm_obs in get_action method

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Co-authored-by: Lukas Linauer <lukas@datenvorsprung.at>

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],
@@ -229,6 +229,7 @@ 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 not is_deterministic else dist.mean
         actions = actions.clamp(*self.action_range).to(self.device)

rl_games/algos_torch/sac_agent.py

@@ -208,6 +208,8 @@ 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):