"--task",
     type=str,
     default="twotargets",
-    help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, escape",
+    help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
 parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
 
 ##############################
-# escape options
+# greed options
 
-parser.add_argument("--escape_height", type=int, default=5)
+parser.add_argument("--greed_height", type=int, default=5)
 
-parser.add_argument("--escape_width", type=int, default=7)
+parser.add_argument("--greed_width", type=int, default=7)
 
-parser.add_argument("--escape_T", type=int, default=25)
+parser.add_argument("--greed_T", type=int, default=25)
 
-parser.add_argument("--escape_nb_walls", type=int, default=5)
+parser.add_argument("--greed_nb_walls", type=int, default=5)
 
 ######################################################################
 
         "nb_train_samples": 60000,
         "nb_test_samples": 10000,
     },
-    "escape": {
+    "greed": {
         "model": "37M",
         "batch_size": 25,
         "nb_train_samples": 25000,
         device=device,
     )
 
-elif args.task == "escape":
-    task = tasks.Escape(
+elif args.task == "greed":
+    task = tasks.Greed(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
-        height=args.escape_height,
-        width=args.escape_width,
-        T=args.escape_T,
-        nb_walls=args.escape_nb_walls,
+        height=args.greed_height,
+        width=args.greed_width,
+        T=args.greed_T,
+        nb_walls=args.greed_nb_walls,
         logger=log_string,
         device=device,
     )
 
 
 ######################################################################
 
-import escape
+import greed
 
 
-class Escape(Task):
+class Greed(Task):
     def __init__(
         self,
         nb_train_samples,
         self.height = height
         self.width = width
 
-        states, actions, rewards = escape.generate_episodes(
+        states, actions, rewards = greed.generate_episodes(
             nb_train_samples + nb_test_samples, height, width, T, nb_walls
         )
-        seq = escape.episodes2seq(states, actions, rewards)
+        seq = greed.episodes2seq(states, actions, rewards)
         # seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3]
         self.train_input = seq[:nb_train_samples].to(self.device)
         self.test_input = seq[nb_train_samples:].to(self.device)
                 t % self.it_len == self.index_lookahead_reward
             ).long()
 
-            batch = lr_mask * escape.lookahead_reward2code(2) + (1 - lr_mask) * batch
+            batch = lr_mask * greed.lookahead_reward2code(2) + (1 - lr_mask) * batch
             yield batch
 
     def vocabulary_size(self):
-        return escape.nb_codes
+        return greed.nb_codes
 
     def thinking_autoregression(
         self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
         # Erase all the content but that of the first iteration
         result[:, self.it_len :] = -1
         # Set the lookahead_reward of the firs to UNKNOWN
-        result[:, self.index_lookahead_reward] = escape.lookahead_reward2code(2)
+        result[:, self.index_lookahead_reward] = greed.lookahead_reward2code(2)
 
         t = torch.arange(result.size(1), device=result.device)[None, :]
 
             if u > 0:
                 result[
                     :, u + self.index_lookahead_reward
-                ] = escape.lookahead_reward2code(2)
+                ] = greed.lookahead_reward2code(2)
                 ar_mask = (t >= u + self.index_states).long() * (
                     t < u + self.index_states + self.state_len
                 ).long()
                 ar(result, ar_mask)
 
             # Generate the action and reward with lookahead_reward to +1
-            result[:, u + self.index_lookahead_reward] = escape.lookahead_reward2code(1)
+            result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(1)
             ar_mask = (t >= u + self.index_action).long() * (
                 t <= u + self.index_reward
             ).long()
             ar(result, ar_mask)
 
             # Set the lookahead_reward to UNKNOWN for the next iterations
-            result[:, u + self.index_lookahead_reward] = escape.lookahead_reward2code(2)
+            result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(2)
 
         filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
         with open(filename, "w") as f:
             for n in range(10):
                 for s in snapshots:
-                    lr, s, a, r = escape.seq2episodes(
+                    lr, s, a, r = greed.seq2episodes(
                         s[n : n + 1], self.height, self.width
                     )
-                    str = escape.episodes2str(
+                    str = greed.episodes2str(
                         lr, s, a, r, unicode=True, ansi_colors=True
                     )
                     f.write(str)
 
         # Saving the generated sequences
 
-        lr, s, a, r = escape.seq2episodes(result, self.height, self.width)
-        str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+        lr, s, a, r = greed.seq2episodes(result, self.height, self.width)
+        str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
 
         filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
         with open(filename, "w") as f:
 
         # Saving the ground truth
 
-        lr, s, a, r = escape.seq2episodes(
+        lr, s, a, r = greed.seq2episodes(
             result,
             self.height,
             self.width,
         )
-        str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+        str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
 
         filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
         with open(filename, "w") as f:
 
         # Saving the generated sequences
 
-        lr, s, a, r = escape.seq2episodes(
+        lr, s, a, r = greed.seq2episodes(
             result,
             self.height,
             self.width,
         )
-        str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+        str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
 
         filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
         with open(filename, "w") as f: