Do burn-in for PPO

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

         old_log_probs = ActionLogProbs.from_buffer(batch).flatten()
         log_probs = log_probs.flatten()
         loss_masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS], dtype=torch.bool)
+        seq_len = self.policy.sequence_length
+        if seq_len > 1:
+            # Do burn-in
+            _burn_in_percent = 0.2
+            burn_in_mask = torch.ones_like(loss_masks)
+            burn_in_amt = int(seq_len * _burn_in_percent)
+            for i in range(batch.num_experiences // seq_len):
+                burn_in_mask[seq_len * i : seq_len * i + burn_in_amt] = 0.0
+            loss_masks = loss_masks * burn_in_mask
         value_loss = ModelUtils.trust_region_value_loss(
             values, old_values, returns, decay_eps, loss_masks
         )