X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=escape.py;h=f51863b0c42e9c4c0d38fa334296c6ebfca9ed04;hb=19ec7f3e4030ddece2647983dcf1bed5eb0d9544;hp=43843f0bfbea776410b42cccaf56c534a673e150;hpb=9eadd2cd6913a0de53b4b0f526157497e8d14381;p=picoclvr.git

diff --git a/escape.py b/escape.py
index 43843f0..f51863b 100755
--- a/escape.py
+++ b/escape.py
@@ -94,7 +94,7 @@ def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3):
         )
         hit = (hit > 0).long()
 
-        assert hit.min() == 0 and hit.max() <= 1
+        # assert hit.min() == 0 and hit.max() <= 1
 
         rewards[:, t + 1] = -hit + (1 - hit) * agent[:, t + 1, -1, -1]
 
@@ -133,27 +133,27 @@ def episodes2seq(states, actions, rewards, lookahead_delta=None):
     r = rewards[:, :, None]
     rewards = (r + 1) + first_rewards_code
 
-    assert (
-        states.min() >= first_state_code
-        and states.max() < first_state_code + nb_state_codes
-    )
-    assert (
-        actions.min() >= first_actions_code
-        and actions.max() < first_actions_code + nb_actions_codes
-    )
-    assert (
-        rewards.min() >= first_rewards_code
-        and rewards.max() < first_rewards_code + nb_rewards_codes
-    )
+    # assert (
+    # states.min() >= first_state_code
+    # and states.max() < first_state_code + nb_state_codes
+    # )
+    # assert (
+    # actions.min() >= first_actions_code
+    # and actions.max() < first_actions_code + nb_actions_codes
+    # )
+    # assert (
+    # rewards.min() >= first_rewards_code
+    # and rewards.max() < first_rewards_code + nb_rewards_codes
+    # )
 
     if lookahead_delta is None:
         return torch.cat([states, actions, rewards], dim=2).flatten(1)
     else:
-        assert (
-            lookahead_rewards.min() >= first_lookahead_rewards_code
-            and lookahead_rewards.max()
-            < first_lookahead_rewards_code + nb_lookahead_rewards_codes
-        )
+        # assert (
+        # lookahead_rewards.min() >= first_lookahead_rewards_code
+        # and lookahead_rewards.max()
+        # < first_lookahead_rewards_code + nb_lookahead_rewards_codes
+        # )
         return torch.cat([states, actions, rewards, lookahead_rewards], dim=2).flatten(
             1
         )