Fixed applying minibatch_size_per_env (#287)

rl_games/common/a2c_common.py

@@ -236,7 +236,6 @@ def __init__(self, base_name, params):
         self.game_shaped_rewards = torch_ext.AverageMeter(self.value_size, self.games_to_track).to(self.ppo_device)
         self.game_lengths = torch_ext.AverageMeter(1, self.games_to_track).to(self.ppo_device)
         self.obs = None
-        self.games_num = self.config['minibatch_size'] // self.seq_length # it is used only for current rnn implementation
 
         self.batch_size = self.horizon_length * self.num_actors * self.num_agents
         self.batch_size_envs = self.horizon_length * self.num_actors
@@ -245,6 +244,16 @@ def __init__(self, base_name, params):
         self.minibatch_size_per_env = self.config.get('minibatch_size_per_env', 0)
         self.minibatch_size = self.config.get('minibatch_size', self.num_actors * self.minibatch_size_per_env)
 
+        # either minibatch_size_per_env or minibatch_size should be present in a config
+        # if both are present, minibatch_size is used
+        # otherwise minibatch_size_per_env is used minibatch_size_per_env is used to calculate minibatch_size
+        self.minibatch_size_per_env = self.config.get('minibatch_size_per_env', 0)
+        self.minibatch_size = self.config.get('minibatch_size', self.num_actors * self.minibatch_size_per_env)
+
+        assert(self.minibatch_size > 0)
+
+        self.games_num = self.minibatch_size // self.seq_length # it is used only for current rnn implementation
+
         self.num_minibatches = self.batch_size // self.minibatch_size
         assert(self.batch_size % self.minibatch_size == 0)