X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=escape.py;h=f51863b0c42e9c4c0d38fa334296c6ebfca9ed04;hb=19ec7f3e4030ddece2647983dcf1bed5eb0d9544;hp=a2e378d882814c14192b664829ce0081b3583cb8;hpb=49218a3e2adb19d2ec98a454e0076c3461ab1c69;p=picoclvr.git

diff --git a/escape.py b/escape.py
index a2e378d..f51863b 100755
--- a/escape.py
+++ b/escape.py
@@ -25,7 +25,7 @@ nb_codes = first_lookahead_rewards_code + nb_lookahead_rewards_codes
 ######################################################################
 
 
-def generate_episodes(nb, height=6, width=6, T=10):
+def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3):
     rnd = torch.rand(nb, height, width)
     rnd[:, 0, :] = 0
     rnd[:, -1, :] = 0
@@ -33,11 +33,12 @@ def generate_episodes(nb, height=6, width=6, T=10):
     rnd[:, :, -1] = 0
     wall = 0
 
-    for k in range(3):
+    for k in range(nb_walls):
         wall = wall + (
             rnd.flatten(1).argmax(dim=1)[:, None]
             == torch.arange(rnd.flatten(1).size(1))[None, :]
         ).long().reshape(rnd.size())
+
         rnd = rnd * (1 - wall.clamp(max=1))
 
     states = wall[:, None, :, :].expand(-1, T, -1, -1).clone()
@@ -93,7 +94,7 @@ def generate_episodes(nb, height=6, width=6, T=10):
         )
         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]
 
@@ -110,40 +111,49 @@ 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
+        # 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):
+            a[:, t] = rewards[:, t + 1 :].min(dim=-1).values
+            b[:, t] = rewards[:, t + 1 :].max(dim=-1).values
         s = (a < 0).long() * a + (a >= 0).long() * b
         lookahead_rewards = (1 + s[:, :, None]) + first_lookahead_rewards_code
 
     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
         )
@@ -191,7 +201,7 @@ def episodes2str(
     states, actions, rewards, lookahead_rewards=None, unicode=False, ansi_colors=False
 ):
     if unicode:
-        symbols = " █@$"
+        symbols = "·█@$"
         # vert, hori, cross, thin_hori = "║", "═", "╬", "─"
         vert, hori, cross, thin_vert, thin_hori = "┃", "━", "╋", "│", "─"
     else:
@@ -218,7 +228,7 @@ def episodes2str(
                 + "\n"
             )
 
-        result += (vert + thin_hori * states.size(-1)) * states.size(1) + vert + "\n"
+        # result += (vert + thin_hori * states.size(-1)) * states.size(1) + vert + "\n"
 
         def status_bar(a, r, lr=None):
             a, r = a.item(), r.item()
@@ -271,11 +281,11 @@ def episodes2str(
 ######################################################################
 
 if __name__ == "__main__":
-    nb, height, width, T = 10, 4, 6, 20
-    states, actions, rewards = generate_episodes(nb, height, width, T)
+    nb, height, width, T, nb_walls = 25, 5, 7, 25, 5
+    states, actions, rewards = generate_episodes(nb, height, width, T, nb_walls)
     seq = episodes2seq(states, actions, rewards, lookahead_delta=T)
     s, a, r, lr = seq2episodes(seq, height, width, lookahead=True)
     print(episodes2str(s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True))
-    print()
-    for s in seq2str(seq):
-        print(s)
+    # print()
+    # for s in seq2str(seq):
+    # print(s)