diff --git a/legged_gym/legged_gym/scripts/play_vel.py b/legged_gym/legged_gym/scripts/play_vel.py
index 34b6e03..295f798 100644
--- a/legged_gym/legged_gym/scripts/play_vel.py
+++ b/legged_gym/legged_gym/scripts/play_vel.py
@@ -113,7 +113,7 @@ def play(args):
     
     if "one_obstacle_per_track" in env_cfg.terrain.BarrierTrack_kwargs.keys():
         env_cfg.terrain.BarrierTrack_kwargs.pop("one_obstacle_per_track")
-    env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 2# 2
+    env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 1# 2
     env_cfg.commands.ranges.lin_vel_x = [3.0, 3.0] # [1.2, 1.2]
     env_cfg.terrain.BarrierTrack_kwargs['track_block_length']= 2.
     if "distill" in args.task:
diff --git a/rsl_rl/rsl_rl/algorithms/ppo.py b/rsl_rl/rsl_rl/algorithms/ppo.py
index ddb3592..92f7910 100644
--- a/rsl_rl/rsl_rl/algorithms/ppo.py
+++ b/rsl_rl/rsl_rl/algorithms/ppo.py
@@ -238,7 +238,7 @@ class PPO:
         if current_learning_iteration is None:
             vel_loss = 0
         else:
-            vel_loss = torch.square(velocity-2).mean() * np.exp(-0.01 * current_learning_iteration + 165)
+            vel_loss = torch.square(velocity-2).mean() * np.exp(-0.001 * current_learning_iteration + 12)
             vel_loss += torch.square(torch.clamp_max(velocity, 1.) - 1).mean()
 
         return_ = dict(