Fix done in pusht, Fix --time in sbatch

lerobot/common/datasets/pusht.py

@@ -88,10 +88,6 @@ def add_tee(
 
 class PushtExperienceReplay(TensorDictReplayBuffer):
 
-    # available_datasets = [
-    #     "xarm_lift_medium",
-    # ]
-
     def __init__(
         self,
         dataset_id,
@@ -233,6 +229,7 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
             block_angle = state[:, 4]
 
             reward = torch.zeros(num_frames, 1)
+            success = torch.zeros(num_frames, 1, dtype=torch.bool)
             done = torch.zeros(num_frames, 1, dtype=torch.bool)
             for i in range(num_frames):
                 space = pymunk.Space()
@@ -257,7 +254,10 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
                 goal_area = goal_geom.area
                 coverage = intersection_area / goal_area
                 reward[i] = np.clip(coverage / SUCCESS_THRESHOLD, 0, 1)
-                done[i] = coverage > SUCCESS_THRESHOLD
+                success[i] = coverage > SUCCESS_THRESHOLD
+
+            # last step of demonstration is considered done
+            done[-1] = True
 
             episode = TensorDict(
                 {
@@ -271,6 +271,7 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
                     # TODO: verify that reward and done are aligned with image and agent_pos
                     ("next", "reward"): reward[1:],
                     ("next", "done"): done[1:],
+                    ("next", "success"): success[1:],
                 },
                 batch_size=num_frames - 1,
             )

sbatch.sh

@@ -3,7 +3,7 @@
 #SBATCH --ntasks-per-node=1  # number of tasks per node (here 8 tasks, or 1 task per GPU)
 #SBATCH --gres=gpu:1         # number of GPUs reserved per node (here 8, or all the GPUs)
 #SBATCH --cpus-per-task=8    # number of cores per task (8x8 = 64 cores, or all the cores)
-#SBATCH --time=02:00:00
+#SBATCH --time=2-00:00:00
 #SBATCH --output=/home/rcadene/slurm/%j.out
 #SBATCH --error=/home/rcadene/slurm/%j.err
 #SBATCH --qos=low