Update.

diff --git a/escape.py b/escape.py
index f51863b..6f4af35 100755 (executable)
--- a/escape.py
+++ b/escape.py
@@ -25,6 +25,33 @@ nb_codes = first_lookahead_rewards_code + nb_lookahead_rewards_codes
 ######################################################################
 
 
+def action2code(r):
+    return first_actions_code + r
+
+
+def code2action(r):
+    return r - first_actions_code
+
+
+def reward2code(r):
+    return first_rewards_code + r + 1
+
+
+def code2reward(r):
+    return r - first_rewards_code - 1
+
+
+def lookahead_reward2code(r):
+    return first_lookahead_rewards_code + r + 1
+
+
+def code2lookahead_reward(r):
+    return r - first_lookahead_rewards_code - 1
+
+
+######################################################################
+
+
 def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3):
     rnd = torch.rand(nb, height, width)
     rnd[:, 0, :] = 0
@@ -111,17 +138,6 @@ def episodes2seq(states, actions, rewards, lookahead_delta=None):
     actions = actions[:, :, None] + first_actions_code
 
     if lookahead_delta is not None:
-        # r = rewards
-        # u = F.pad(r, (0, lookahead_delta - 1)).as_strided(
-        # (r.size(0), r.size(1), lookahead_delta),
-        # (r.size(1) + lookahead_delta - 1, 1, 1),
-        # )
-        # a = u[:, :, 1:].min(dim=-1).values
-        # b = u[:, :, 1:].max(dim=-1).values
-        # s = (a < 0).long() * a + (a >= 0).long() * b
-        # lookahead_rewards = (1 + s[:, :, None]) + first_lookahead_rewards_code
-
-        # a[n,t]=min_s>t r[n,s]
         a = rewards.new_zeros(rewards.size())
         b = rewards.new_zeros(rewards.size())
         for t in range(a.size(1) - 1):

diff --git a/tasks.py b/tasks.py
index 6b6b8f2..29f1e5a 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -1944,13 +1944,14 @@ class Escape(Task):
         # Generate iteration after iteration
 
         optimistic_bias = result.new_zeros(self.nb_codes, device=result.device)
-        optimistic_bias[(-1) + escape.first_lookahead_rewards_code + 1] = math.log(1e-1)
-        optimistic_bias[(1) + escape.first_lookahead_rewards_code + 1] = math.log(1e1)
+        optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1)
+        optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1)
 
         for u in tqdm.tqdm(
             range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking"
         ):
-            # Generate the lookahead_reward pessimistically
+            # Re-generate the lookahead_reward pessimistically in the
+            # previous iterations
             ar_mask = (t < u).long() * (t % it_len == index_lookahead_reward).long()
             ar(result, ar_mask, logit_biases=-optimistic_bias)
 
@@ -1958,7 +1959,8 @@ class Escape(Task):
             ar_mask = (t >= u).long() * (t < u + state_len).long()
             ar(result, ar_mask)
 
-            # Generate the lookahead_reward optimistically
+            # Re-generate the lookahead_reward optimistically in the
+            # previous iterations
             ar_mask = (t < u).long() * (t % it_len == index_lookahead_reward).long()
             ar(result, ar_mask, logit_biases=optimistic_bias)