Improved naming for fast pbt.

rl_games/algos_torch/sac_agent.py

@@ -61,7 +61,6 @@ def __init__(self, base_name, params):
             'action_dim': self.env_info["action_space"].shape[0],
             'actions_num' : self.actions_num,
             'input_shape' : obs_shape,
-            'normalize_input' : self.normalize_input,
             'normalize_input': self.normalize_input,
         }
         self.model = self.network.build(net_config)
@@ -224,6 +223,7 @@ def set_weights(self, weights):
             self.model.running_mean_std.load_state_dict(weights['running_mean_std'])
 
     def get_full_state_weights(self):
+        breakpoint()
         print("Loading full weights")
         state = self.get_weights()
 
@@ -246,14 +246,21 @@ def set_full_state_weights(self, weights, set_epoch=True):
         self.critic_optimizer.load_state_dict(weights['critic_optimizer'])
         self.log_alpha_optimizer.load_state_dict(weights['log_alpha_optimizer'])
 
+        self.last_mean_rewards = weights.get('last_mean_rewards', -1000000000)
+
+        if self.vec_env is not None:
+            env_state = weights.get('env_state', None)
+            self.vec_env.set_env_state(env_state)
+
     def restore(self, fn, set_epoch=True):
+        print("SAC restore")
         checkpoint = torch_ext.load_checkpoint(fn)
         self.set_full_state_weights(checkpoint, set_epoch=set_epoch)
 
-    def get_params(self, param_name):
+    def get_param(self, param_name):
         pass
 
-    def set_params(self, param_name, param_value):
+    def set_param(self, param_name, param_value):
         pass
 
     def get_masked_action_values(self, obs, action_masks):

rl_games/common/a2c_common.py

@@ -660,7 +660,7 @@ def set_weights(self, weights):
         self.model.load_state_dict(weights['model'])
         self.set_stats_weights(weights)
 
-    def get_params(self, param_name):
+    def get_param(self, param_name):
         if param_name in [
             "grad_norm",
             "critic_coef", 
@@ -678,7 +678,7 @@ def get_params(self, param_name):
         else:
             raise NotImplementedError(f"Can't get param {param_name}")       
 
-    def set_params(self, param_name, param_value):
+    def set_param(self, param_name, param_value):
         if param_name in [
             "grad_norm",
             "critic_coef", 

rl_games/common/player.py

@@ -364,9 +364,9 @@ def run(self):
                         cur_rewards_done = cur_rewards/done_count
                         cur_steps_done = cur_steps/done_count
                         if print_game_res:
-                            print(f'reward: {cur_rewards_done:.4} steps: {cur_steps_done:.4} w: {game_res}')
+                            print(f'reward: {cur_rewards_done:.2f} steps: {cur_steps_done:.1f} w: {game_res}')
                         else:
-                            print(f'reward: {cur_rewards_done:.4} steps: {cur_steps_done:.4f}')
+                            print(f'reward: {cur_rewards_done:.2f} steps: {cur_steps_done:.1f}')
 
                     sum_game_res += game_res
                     if batch_size//self.num_agents == 1 or games_played >= n_games:

rl_games/interfaces/base_algorithm.py

@@ -40,12 +40,12 @@ def set_weights(self, weights):
 
     # Get algo training parameters
     @abstractmethod
-    def get_params(self, param_name):
+    def get_param(self, param_name):
         pass
 
     # Set algo training parameters
     @abstractmethod
-    def set_params(self, param_name, param_value):
+    def set_param(self, param_name, param_value):
         pass
 
 

rl_games/torch_runner.py

@@ -53,8 +53,6 @@ def __init__(self, algo_observer=None):
         #torch.backends.cudnn.deterministic = True
         #torch.use_deterministic_algorithms(True)
 
-        #breakpoint()
-
     def reset(self):
         pass
 
@@ -120,7 +118,7 @@ def run_train(self, args):
         agent.train()
 
     def run_play(self, args):
-        print('Started to play')
+        breakpoint()
         player = self.create_player()
         _restore(player, args)
         _override_sigma(player, args)
@@ -137,7 +135,6 @@ def run(self, args):
             print('Started to train')
             self.run_train(args)
         elif args['play']:
-            print('Started to play')
             self.run_play(args)
         else:
             print('Started to train2')