More seq_length work.

rl_games/algos_torch/central_value.py

@@ -12,7 +12,7 @@
 class CentralValueTrain(nn.Module):
 
     def __init__(self, state_shape, value_size, ppo_device, num_agents, horizon_length, num_actors, num_actions, 
-                seq_len, normalize_value, network, config, writter, max_epochs, multi_gpu, zero_rnn_on_done):
+                seq_length, normalize_value, network, config, writter, max_epochs, multi_gpu, zero_rnn_on_done):
         nn.Module.__init__(self)
 
         self.ppo_device = ppo_device

rl_games/common/a2c_common.py

@@ -202,7 +202,10 @@ def __init__(self, base_name, params):
         self.rewards_shaper = config['reward_shaper']
         self.num_agents = self.env_info.get('agents', 1)
         self.horizon_length = config['horizon_length']
+
+        # seq_length is used only with rnn policy and value functions
         self.seq_length = self.config.get('seq_length', 4)
+        print('seq_length:', self.seq_length)
         self.bptt_len = self.config.get('bptt_length', self.seq_length) # not used right now. Didn't show that it is usefull
         self.zero_rnn_on_done = self.config.get('zero_rnn_on_done', True)
         self.normalize_advantage = config['normalize_advantage']
@@ -794,7 +797,7 @@ def play_steps_rnn(self):
         for n in range(self.horizon_length):
             if n % self.seq_length == 0:
                 for s, mb_s in zip(self.rnn_states, mb_rnn_states):
-                    mb_s[n // self.seq_len,:,:,:] = s
+                    mb_s[n // self.seq_length,:,:,:] = s
 
             if self.has_central_value:
                 self.central_value_net.pre_step_rnn(n)

rl_games/common/datasets.py

@@ -15,9 +15,9 @@ def __init__(self, batch_size, minibatch_size, is_discrete, is_rnn, device, seq_
         self.is_discrete = is_discrete
         self.is_continuous = not is_discrete
         total_games = self.batch_size // self.seq_length
-        self.num_games_batch = self.minibatch_size // self.seq_len
+        self.num_games_batch = self.minibatch_size // self.seq_length
         self.game_indexes = torch.arange(total_games, dtype=torch.long, device=self.device)
-        self.flat_indexes = torch.arange(total_games * self.seq_len, dtype=torch.long, device=self.device).reshape(total_games, self.seq_length)
+        self.flat_indexes = torch.arange(total_games * self.seq_length, dtype=torch.long, device=self.device).reshape(total_games, self.seq_length)
 
         self.special_names = ['rnn_states']
 
@@ -36,9 +36,10 @@ def __len__(self):
     def _get_item_rnn(self, idx):
         gstart = idx * self.num_games_batch
         gend = (idx + 1) * self.num_games_batch
-        start = gstart * self.seq_len
-        end = gend * self.seq_len
-        self.last_range = (start, end)   
+        start = gstart * self.seq_length
+        end = gend * self.seq_length
+        self.last_range = (start, end)
+
         input_dict = {}
         for k,v in self.values_dict.items():
             if k not in self.special_names: