redo so that max timesteps is treated as truncation at the last timestep, then allow for accepting the truncation signal from the environment and reuse same logic

dreamer4/dreamer4.py

@@ -2133,13 +2133,18 @@ class DynamicsWorldModel(Module):
         # keep track of termination, for setting the `is_truncated` field on Experience and for early stopping interaction with env
 
         is_terminated = full((batch,), False, device = device)
+        is_truncated = full((batch,), False, device = device)
+
         episode_lens = full((batch,), 0, device = device)
 
         # maybe time kv cache
 
         time_kv_cache = None
 
-        for i in range(max_timesteps + 1):
+        step_index = 0
+
+        while not is_terminated.all():
+            step_index += 1
 
             latents = self.video_tokenizer(video, return_latents = True)
 
@@ -2201,10 +2206,15 @@ class DynamicsWorldModel(Module):
 
             if len(env_step_out) == 2:
                 next_frame, reward = env_step_out
-                terminate = full((batch,), False)
+                terminated = full((batch,), False)
+                truncated = full((batch,), False)
 
             elif len(env_step_out) == 3:
-                next_frame, reward, terminate = env_step_out
+                next_frame, reward, terminated = env_step_out
+                truncated = full((batch,), False)
+
+            elif len(env_step_out) == 4:
+                next_frame, reward, terminated, truncated = env_step_out
 
             # update episode lens
 
@@ -2212,7 +2222,20 @@ class DynamicsWorldModel(Module):
 
             # update `is_terminated`
 
-            is_terminated |= terminate
+            # (1) - environment says it is terminated
+            # (2) - previous step is truncated (this step is for bootstrap value)
+
+            is_terminated |= (terminated | is_truncated)
+
+            # update `is_truncated`
+
+            if step_index <= max_timesteps:
+                is_truncated |= truncated
+
+            if step_index == max_timesteps:
+                # if the step index is at the max time step allowed, set the truncated flag, if not already terminated
+
+                is_truncated |= ~is_terminated
 
             # batch and time dimension
 
@@ -2228,11 +2251,6 @@ class DynamicsWorldModel(Module):
             video = cat((video, next_frame), dim = 2)
             rewards = safe_cat((rewards, reward), dim = 1)
 
-            # early break out if all terminated
-
-            if is_terminated.all():
-                break
-
         # package up one experience for learning
 
         batch, device = latents.shape[0], latents.device
@@ -2246,7 +2264,7 @@ class DynamicsWorldModel(Module):
             values = values,
             step_size = step_size,
             agent_index = agent_index,
-            is_truncated = ~is_terminated,
+            is_truncated = is_truncated,
             lens = episode_lens,
             is_from_world_model = False
         )

dreamer4/mocks.py

@@ -22,7 +22,9 @@ class MockEnv(Module):
         num_envs = 1,
         vectorized = False,
         terminate_after_step = None,
-        rand_terminate_prob = 0.05
+        rand_terminate_prob = 0.05,
+        can_truncate = False,
+        rand_truncate_prob = 0.05,
     ):
         super().__init__()
         self.image_shape = image_shape
@@ -32,12 +34,15 @@ class MockEnv(Module):
         self.vectorized = vectorized
         assert not (vectorized and num_envs == 1)
 
-        # mocking termination
+        # mocking termination and truncation
 
         self.can_terminate = exists(terminate_after_step)
         self.terminate_after_step = terminate_after_step
         self.rand_terminate_prob = rand_terminate_prob
 
+        self.can_truncate = can_truncate
+        self.rand_truncate_prob = rand_truncate_prob
+
         self.register_buffer('_step', tensor(0))
 
     def get_random_state(self):
@@ -72,16 +77,21 @@ class MockEnv(Module):
 
         out = (state, reward)
 
+
         if self.can_terminate:
             shape = (self.num_envs,) if self.vectorized else (1,)
+            valid_step = self._step > self.terminate_after_step
 
-            terminate = (
-                (torch.rand(shape) < self.rand_terminate_prob) &
-                (self._step > self.terminate_after_step)
-            )
+            terminate = (torch.rand(shape) < self.rand_terminate_prob) & valid_step
 
             out = (*out, terminate)
 
+            # maybe truncation
+
+            if self.can_truncate:
+                truncate = (torch.rand(shape) < self.rand_truncate_prob) & valid_step & ~terminate
+                out = (*out, truncate)
+
         self._step.add_(1)
 
         return out

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.0.82"
+version = "0.0.83"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

tests/test_dreamer.py

@@ -613,10 +613,12 @@ def test_cache_generate():
 @param('vectorized', (False, True))
 @param('use_signed_advantage', (False, True))
 @param('env_can_terminate', (False, True))
+@param('env_can_truncate', (False, True))
 def test_online_rl(
     vectorized,
     use_signed_advantage,
-    env_can_terminate
+    env_can_terminate,
+    env_can_truncate
 ):
     from dreamer4.dreamer4 import DynamicsWorldModel, VideoTokenizer
 
@@ -656,6 +658,7 @@ def test_online_rl(
         vectorized = vectorized,
         num_envs = 4,
         terminate_after_step = 2 if env_can_terminate else None,
+        can_truncate = env_can_truncate,
         rand_terminate_prob = 0.1
     )