handle environments that return a terminate flag, also make sure episode lens are logged in vectorized env

dreamer4/dreamer4.py

@@ -2118,6 +2118,8 @@ class DynamicsWorldModel(Module):
         else:
             video = rearrange(init_frame, 'c vh vw -> 1 c 1 vh vw')
 
+        batch, device = video.shape[0], video.device
+
         # accumulate
 
         rewards = None
@@ -2128,6 +2130,11 @@ class DynamicsWorldModel(Module):
         values = None
         latents = None
 
+        # 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)
+        episode_lens = full((batch,), 0, device = device)
+
         # maybe time kv cache
 
         time_kv_cache = None
@@ -2190,7 +2197,22 @@ class DynamicsWorldModel(Module):
 
             # pass the sampled action to the environment and get back next state and reward
 
-            next_frame, reward = env.step((sampled_discrete_actions, sampled_continuous_actions))
+            env_step_out = env.step((sampled_discrete_actions, sampled_continuous_actions))
+
+            if len(env_step_out) == 2:
+                next_frame, reward = env_step_out
+                terminate = full((batch,), False)
+
+            elif len(env_step_out) == 3:
+                next_frame, reward, terminate = env_step_out
+
+            # update episode lens
+
+            episode_lens = torch.where(is_terminated, episode_lens, episode_lens + 1)
+
+            # update `is_terminated`
+
+            is_terminated |= terminate
 
             # batch and time dimension
 
@@ -2206,6 +2228,11 @@ 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
@@ -2219,7 +2246,8 @@ class DynamicsWorldModel(Module):
             values = values,
             step_size = step_size,
             agent_index = agent_index,
-            lens = full((batch,), max_timesteps + 1, device = device),
+            is_truncated = ~is_terminated,
+            lens = episode_lens,
             is_from_world_model = False
         )
 

dreamer4/mocks.py

@@ -7,6 +7,11 @@ from torch.nn import Module
 
 from einops import repeat
 
+# helpers
+
+def exists(v):
+    return v is not None
+
 # mock env
 
 class MockEnv(Module):
@@ -15,7 +20,9 @@ class MockEnv(Module):
         image_shape,
         reward_range = (-100, 100),
         num_envs = 1,
-        vectorized = False
+        vectorized = False,
+        terminate_after_step = None,
+        rand_terminate_prob = 0.05
     ):
         super().__init__()
         self.image_shape = image_shape
@@ -25,6 +32,12 @@ class MockEnv(Module):
         self.vectorized = vectorized
         assert not (vectorized and num_envs == 1)
 
+        # mocking termination
+
+        self.can_terminate = exists(terminate_after_step)
+        self.terminate_after_step = terminate_after_step
+        self.rand_terminate_prob = rand_terminate_prob
+
         self.register_buffer('_step', tensor(0))
 
     def get_random_state(self):
@@ -50,13 +63,23 @@ class MockEnv(Module):
 
         reward = empty(()).uniform_(*self.reward_range)
 
-        if not self.vectorized:
-            return state, reward
+        if self.vectorized:
+            discrete, continuous = actions
+            assert discrete.shape[0] == self.num_envs, f'expected batch of actions for {self.num_envs} environments'
 
-        discrete, continuous = actions
-        assert discrete.shape[0] == self.num_envs, f'expected batch of actions for {self.num_envs} environments'
+            state = repeat(state, '... -> b ...', b = self.num_envs)
+            reward = repeat(reward, ' -> b', b = self.num_envs)
 
-        state = repeat(state, '... -> b ...', b = self.num_envs)
-        reward = repeat(reward, ' -> b', b = self.num_envs)
+        out = (state, reward)
 
-        return state, reward
+        if self.can_terminate:
+            terminate = (
+                (torch.rand((self.num_envs)) < self.rand_terminate_prob) &
+                (self._step > self.terminate_after_step)
+            )
+
+            out = (*out, terminate)
+
+        self._step.add_(1)
+
+        return out

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.0.81"
+version = "0.0.82"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

tests/test_dreamer.py

@@ -612,9 +612,11 @@ def test_cache_generate():
 
 @param('vectorized', (False, True))
 @param('use_signed_advantage', (False, True))
+@param('env_can_terminate', (False, True))
 def test_online_rl(
     vectorized,
-    use_signed_advantage
+    use_signed_advantage,
+    env_can_terminate
 ):
     from dreamer4.dreamer4 import DynamicsWorldModel, VideoTokenizer
 
@@ -649,7 +651,13 @@ def test_online_rl(
     from dreamer4.mocks import MockEnv
     from dreamer4.dreamer4 import combine_experiences
 
-    mock_env = MockEnv((256, 256), vectorized = vectorized, num_envs = 4)
+    mock_env = MockEnv(
+        (256, 256),
+        vectorized = vectorized,
+        num_envs = 4,
+        terminate_after_step = 2 if env_can_terminate else None,
+        rand_terminate_prob = 0.1
+    )
 
     # manually