README.md

@@ -1,99 +1,10 @@
 <img src="./dreamer4-fig2.png" width="400px"></img>
 
-## Dreamer 4
+## Dreamer 4 (wip)
 
 Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v1) for his [Dreamer](https://danijar.com/project/dreamer4/) line of work
 
-[Discord channel](https://discord.gg/PmGR7KRwxq) for collaborating with other researchers interested in this work
+[Temporary Discord](https://discord.gg/MkACrrkrYR)
-
-## Appreciation
-
-- [@dirkmcpherson](https://github.com/dirkmcpherson) for fixes to typo errors and unpassed arguments!
-
-## Install
-
-```bash
-$ pip install dreamer4
-```
-
-## Usage
-
-```python
-import torch
-from dreamer4 import VideoTokenizer, DynamicsWorldModel
-
-# video tokenizer, learned through MAE + lpips
-
-tokenizer = VideoTokenizer(
-    dim = 512,
-    dim_latent = 32,
-    patch_size = 32,
-    image_height = 256,
-    image_width = 256
-)
-
-video = torch.randn(2, 3, 10, 256, 256)
-
-# learn the tokenizer
-
-loss = tokenizer(video)
-loss.backward()
-
-# dynamics world model
-
-world_model = DynamicsWorldModel(
-    dim = 512,
-    dim_latent = 32,
-    video_tokenizer = tokenizer,
-    num_discrete_actions = 4,
-    num_residual_streams = 1
-)
-
-# state, action, rewards
-
-video = torch.randn(2, 3, 10, 256, 256)
-discrete_actions = torch.randint(0, 4, (2, 10, 1))
-rewards = torch.randn(2, 10)
-
-# learn dynamics / behavior cloned model
-
-loss = world_model(
-    video = video,
-    rewards = rewards,
-    discrete_actions = discrete_actions
-)
-
-loss.backward()
-
-# do the above with much data
-
-# then generate dreams
-
-dreams = world_model.generate(
-    10,
-    batch_size = 2,
-    return_decoded_video = True,
-    return_for_policy_optimization = True
-)
-
-# learn from the dreams
-
-actor_loss, critic_loss = world_model.learn_from_experience(dreams)
-
-(actor_loss + critic_loss).backward()
-
-# learn from environment
-
-from dreamer4.mocks import MockEnv
-
-mock_env = MockEnv((256, 256), vectorized = True, num_envs = 4)
-
-experience = world_model.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = True)
-
-actor_loss, critic_loss = world_model.learn_from_experience(experience)
-
-(actor_loss + critic_loss).backward()
-```
 
 ## Citation
 
@@ -108,5 +19,3 @@ actor_loss, critic_loss = world_model.learn_from_experience(experience)
     url     = {https://arxiv.org/abs/2509.24527}, 
 }
 ```
-
-*the conquest of nature is to be achieved through number and measure - angels to Descartes in a dream*

dreamer4/__init__.py

@@ -1,7 +1,6 @@
 from dreamer4.dreamer4 import (
     VideoTokenizer,
-    DynamicsWorldModel,
+    DynamicsWorldModel
-    AxialSpaceTimeTransformer
 )
 
 

dreamer4/trainers.py

@@ -396,20 +396,13 @@ class SimTrainer(Module):
         old_values = experience.values
         rewards = experience.rewards
 
-        has_agent_embed = exists(experience.agent_embed)
-        agent_embed = experience.agent_embed
-
         discrete_actions, continuous_actions = experience.actions
         discrete_log_probs, continuous_log_probs = experience.log_probs
 
-        discrete_old_action_unembeds, continuous_old_action_unembeds = default(experience.old_action_unembeds, (None, None))
-
         # handle empties
 
         empty_tensor = torch.empty_like(rewards)
 
-        agent_embed = default(agent_embed, empty_tensor)
-
         has_discrete = exists(discrete_actions)
         has_continuous = exists(continuous_actions)
 
@@ -419,9 +412,6 @@ class SimTrainer(Module):
         discrete_log_probs = default(discrete_log_probs, empty_tensor)
         continuous_log_probs = default(continuous_log_probs, empty_tensor)
 
-        discrete_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
-        continuous_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
-
         # create the dataset and dataloader
 
         dataset = TensorDataset(
@@ -430,9 +420,6 @@ class SimTrainer(Module):
             continuous_actions,
             discrete_log_probs,
             continuous_log_probs,
-            agent_embed,
-            discrete_old_action_unembeds,
-            continuous_old_action_unembeds,
             old_values,
             rewards
         )
@@ -447,9 +434,6 @@ class SimTrainer(Module):
                 continuous_actions,
                 discrete_log_probs,
                 continuous_log_probs,
-                agent_embed,
-                discrete_old_action_unembeds,
-                continuous_old_action_unembeds,
                 old_values,
                 rewards
             ) in dataloader:
@@ -464,17 +448,10 @@ class SimTrainer(Module):
                     continuous_log_probs if has_continuous else None
                 )
 
-                old_action_unembeds = (
-                    discrete_old_action_unembeds if has_discrete else None,
-                    continuous_old_action_unembeds if has_continuous else None
-                )
-
                 batch_experience = Experience(
                     latents = latents,
                     actions = actions,
                     log_probs = log_probs,
-                    agent_embed = agent_embed if has_agent_embed else None,
-                    old_action_unembeds = old_action_unembeds,
                     values = old_values,
                     rewards = rewards,
                     step_size = step_size,
@@ -528,7 +505,7 @@ class SimTrainer(Module):
 
                 total_experience += num_experience
 
-                experiences.append(experience.cpu())
+                experiences.append(experience)
 
             combined_experiences = combine_experiences(experiences)
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.1.24"
+version = "0.0.90"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }
@@ -36,8 +36,7 @@ dependencies = [
     "hyper-connections>=0.2.1",
     "torch>=2.4",
     "torchvision",
-    "x-mlps-pytorch>=0.0.29",
+    "x-mlps-pytorch>=0.0.29"
-    "vit-pytorch>=1.15.3"
 ]
 
 [project.urls]

tests/test_dreamer.py

@@ -15,8 +15,7 @@ def exists(v):
 @param('condition_on_actions', (False, True))
 @param('num_residual_streams', (1, 4))
 @param('add_reward_embed_to_agent_token', (False, True))
-@param('add_state_pred_head', (False, True))
+@param('use_time_kv_cache', (False, True))
-@param('use_time_cache', (False, True))
 @param('var_len', (False, True))
 def test_e2e(
     pred_orig_latent,
@@ -29,8 +28,7 @@ def test_e2e(
     condition_on_actions,
     num_residual_streams,
     add_reward_embed_to_agent_token,
-    add_state_pred_head,
+    use_time_kv_cache,
-    use_time_cache,
     var_len
 ):
     from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
@@ -43,9 +41,7 @@ def test_e2e(
         patch_size = 32,
         attn_dim_head = 16,
         num_latent_tokens = 4,
-        num_residual_streams = num_residual_streams,
+        num_residual_streams = num_residual_streams
-        encoder_add_decor_aux_loss = True,
-        decorr_sample_frac = 1.
     )
 
     video = torch.randn(2, 3, 4, 256, 256)
@@ -73,13 +69,12 @@ def test_e2e(
         pred_orig_latent = pred_orig_latent,
         num_discrete_actions = 4,
         attn_dim_head = 16,
-        attn_heads = heads,
         attn_kwargs = dict(
+            heads = heads,
             query_heads = query_heads,
         ),
         prob_no_shortcut_train = prob_no_shortcut_train,
         add_reward_embed_to_agent_token = add_reward_embed_to_agent_token,
-        add_state_pred_head = add_state_pred_head,
         num_residual_streams = num_residual_streams
     )
 
@@ -126,7 +121,7 @@ def test_e2e(
         image_width = 128,
         batch_size = 2,
         return_rewards_per_frame = True,
-        use_time_cache = use_time_cache
+        use_time_kv_cache = use_time_kv_cache
     )
 
     assert generations.video.shape == (2, 3, 10, 128, 128)
@@ -351,15 +346,6 @@ def test_action_embedder():
     assert discrete_logits.shape == (2, 3, 8)
     assert continuous_mean_log_var.shape == (2, 3, 2, 2)
 
-    # test kl div
-
-    discrete_logits_tgt, continuous_mean_log_var_tgt = embedder.unembed(action_embed)
-
-    discrete_kl_div, continuous_kl_div = embedder.kl_div((discrete_logits, continuous_mean_log_var), (discrete_logits_tgt, continuous_mean_log_var_tgt))
-
-    assert discrete_kl_div.shape == (2, 3)
-    assert continuous_kl_div.shape == (2, 3)
-
     # return discrete split by number of actions
 
     discrete_logits, continuous_mean_log_var = embedder.unembed(action_embed, return_split_discrete = True)
@@ -620,18 +606,18 @@ def test_cache_generate():
         num_residual_streams = 1
     )
 
-    generated, time_cache = dynamics.generate(1, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, return_time_kv_cache = True)
-    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
-    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
 
 @param('vectorized', (False, True))
-@param('use_pmpo', (False, True))
+@param('use_signed_advantage', (False, True))
 @param('env_can_terminate', (False, True))
 @param('env_can_truncate', (False, True))
 @param('store_agent_embed', (False, True))
 def test_online_rl(
     vectorized,
-    use_pmpo,
+    use_signed_advantage,
     env_can_terminate,
     env_can_truncate,
     store_agent_embed
@@ -646,9 +632,7 @@ def test_online_rl(
         dim_latent = 16,
         patch_size = 32,
         attn_dim_head = 16,
-        num_latent_tokens = 1,
+        num_latent_tokens = 1
-        image_height = 256,
-        image_width = 256,
     )
 
     world_model_and_policy = DynamicsWorldModel(
@@ -682,23 +666,15 @@ def test_online_rl(
 
     # manually
 
-    dream_experience = world_model_and_policy.generate(10, batch_size = 1, store_agent_embed = store_agent_embed, return_for_policy_optimization = True)
-
     one_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
     another_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 16, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
 
-    combined_experience = combine_experiences([dream_experience, one_experience, another_experience])
+    combined_experience = combine_experiences([one_experience, another_experience])
-
-    # quick test moving the experience to different devices
-
-    if torch.cuda.is_available():
-        combined_experience = combined_experience.to(torch.device('cuda'))
-        combined_experience = combined_experience.to(world_model_and_policy.device)
 
     if store_agent_embed:
         assert exists(combined_experience.agent_embed)
 
-    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(combined_experience, use_pmpo = use_pmpo)
+    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(combined_experience, use_signed_advantage = use_signed_advantage)
 
     actor_loss.backward()
     critic_loss.backward()
@@ -810,22 +786,3 @@ def test_epo():
 
     fitness = torch.randn(16,)
     dynamics.evolve_(fitness)
-
-def test_images_to_video_tokenizer():
-    import torch
-    from dreamer4 import VideoTokenizer, DynamicsWorldModel, AxialSpaceTimeTransformer
-
-    tokenizer = VideoTokenizer(
-        dim = 512,
-        dim_latent = 32,
-        patch_size = 32,
-        image_height = 256,
-        image_width = 256,
-        encoder_add_decor_aux_loss = True
-    )
-
-    images = torch.randn(2, 3, 256, 256)
-    loss, (losses, recon_images) = tokenizer(images, return_intermediates = True)
-    loss.backward()
-
-    assert images.shape == recon_images.shape