feature(pu): add pistonball_env, its unittest and qmix config

ding/model/template/qmix.py

@@ -1,10 +1,13 @@
-from typing import Union, List
+from functools import reduce
+from typing import List, Union
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functools import reduce
-from ding.utils import list_split, MODEL_REGISTRY
-from ding.torch_utils import fc_block, MLP
+from ding.torch_utils import MLP, fc_block
+from ding.utils import MODEL_REGISTRY, list_split
+
+from ..common import ConvEncoder
 from .q_learning import DRQN
 
 
@@ -111,7 +114,7 @@ def __init__(
             self,
             agent_num: int,
             obs_shape: int,
-            global_obs_shape: int,
+            global_obs_shape: Union[int, List[int]],
             action_shape: int,
             hidden_size_list: list,
             mixer: bool = True,
@@ -146,8 +149,34 @@ def __init__(
         embedding_size = hidden_size_list[-1]
         self.mixer = mixer
         if self.mixer:
-            self._mixer = Mixer(agent_num, global_obs_shape, embedding_size, activation=activation)
-            self._global_state_encoder = nn.Identity()
+            global_obs_shape_type = self._get_global_obs_shape_type(global_obs_shape)
+
+            if global_obs_shape_type == "flat":
+                self._mixer = Mixer(agent_num, global_obs_shape, embedding_size, activation=activation)
+                self._global_state_encoder = nn.Identity()
+            elif global_obs_shape_type == "image":
+                self._mixer = Mixer(agent_num, embedding_size, embedding_size, activation=activation)
+                self._global_state_encoder = ConvEncoder(
+                    global_obs_shape, hidden_size_list=hidden_size_list, activation=activation, norm_type='BN'
+                )
+            else:
+                raise ValueError(f"Unsupported global_obs_shape: {global_obs_shape}")
+
+    def _get_global_obs_shape_type(self, global_obs_shape: Union[int, List[int]]) -> str:
+        """
+        Overview:
+            Determine the type of global observation shape.
+        Arguments:
+            - global_obs_shape (:obj:`Union[int, List[int]]`): The global observation state.
+        Returns:
+            - obs_shape_type (:obj:`str`): 'flat' for 1D observation or 'image' for 3D observation.
+        """
+        if isinstance(global_obs_shape, int) or (isinstance(global_obs_shape, list) and len(global_obs_shape) == 1):
+            return "flat"
+        elif isinstance(global_obs_shape, list) and len(global_obs_shape) == 3:
+            return "image"
+        else:
+            raise ValueError(f"Unsupported global_obs_shape: {global_obs_shape}")
 
     def forward(self, data: dict, single_step: bool = True) -> dict:
         """
@@ -182,8 +211,16 @@ def forward(self, data: dict, single_step: bool = True) -> dict:
         agent_state, global_state, prev_state = data['obs']['agent_state'], data['obs']['global_state'], data[
             'prev_state']
         action = data.get('action', None)
+        # If single_step is True, add a new dimension at the front of agent_state
+        # This is necessary to maintain the expected input shape for the model,
+        # which requires a time step dimension even when processing a single step.
         if single_step:
-            agent_state, global_state = agent_state.unsqueeze(0), global_state.unsqueeze(0)
+            agent_state = agent_state.unsqueeze(0)
+        # If single_step is True and global_state has 2 dimensions, add a new dimension at the front of global_state
+        # This ensures that global_state has the same number of dimensions as agent_state,
+        # allowing for consistent processing in the forward computation.
+        if single_step and len(global_state.shape) == 2:
+            global_state = global_state.unsqueeze(0)
         T, B, A = agent_state.shape[:3]
         assert len(prev_state) == B and all(
             [len(p) == A for p in prev_state]
@@ -205,15 +242,38 @@ def forward(self, data: dict, single_step: bool = True) -> dict:
         agent_q_act = torch.gather(agent_q, dim=-1, index=action.unsqueeze(-1))
         agent_q_act = agent_q_act.squeeze(-1)  # T, B, A
         if self.mixer:
-            global_state_embedding = self._global_state_encoder(global_state)
+            global_state_embedding = self._process_global_state(global_state)
             total_q = self._mixer(agent_q_act, global_state_embedding)
         else:
-            total_q = agent_q_act.sum(-1)
+            total_q = agent_q_act.sum(dim=-1)
+
         if single_step:
             total_q, agent_q = total_q.squeeze(0), agent_q.squeeze(0)
+
         return {
             'total_q': total_q,
             'logit': agent_q,
             'next_state': next_state,
             'action_mask': data['obs']['action_mask']
         }
+
+    def _process_global_state(self, global_state: torch.Tensor) -> torch.Tensor:
+        """
+        Overview:
+            Process the global state to obtain an embedding.
+        Arguments:
+            - global_state (:obj:`torch.Tensor`): The global state tensor.
+
+        Returns:
+            - global_state_embedding (:obj:`torch.Tensor`): The processed global state embedding.
+        """
+        # If global_state has 5 dimensions, it's likely in the form [batch_size, time_steps, C, H, W]
+        if global_state.dim() == 5:
+            # Reshape and apply the global state encoder
+            batch_time_shape = global_state.shape[:2]  # [batch_size, time_steps]
+            reshaped_state = global_state.view(-1, *global_state.shape[-3:])  # Collapse batch and time dims
+            encoded_state = self._global_state_encoder(reshaped_state)
+            return encoded_state.view(*batch_time_shape, -1)  # Reshape back to [batch_size, time_steps, embedding_dim]
+        else:
+            # For lower-dimensional states, apply the encoder directly
+            return self._global_state_encoder(global_state)

ding/model/template/tests/test_qmix.py

@@ -43,3 +43,34 @@ def test_qmix():
         is_differentiable(loss, qmix_model)
         data.pop('action')
         output = qmix_model(data, single_step=False)
+
+
+@pytest.mark.unittest
+def test_qmix_process_global_state():
+    # Test the behavior of the _process_global_state method with different global_obs_shape types
+    agent_num, obs_dim, global_obs_dim, action_dim = 4, 32, 32 * 4, 9
+    embedding_dim = 64
+
+    # Case 1: Test "flat" type global_obs_shape
+    global_obs_shape = global_obs_dim  # Flat global_obs_shape
+    qmix_model_flat = QMix(agent_num, obs_dim, global_obs_shape, action_dim, [64, 128, embedding_dim], mixer=True)
+
+    # Simulate input for the "flat" type global_state
+    batch_size, time_steps = 3, 8
+    global_state_flat = torch.randn(batch_size, time_steps, global_obs_dim)
+    processed_flat = qmix_model_flat._process_global_state(global_state_flat)
+
+    # Ensure the output shape is correct [batch_size, time_steps, embedding_dim]
+    assert processed_flat.shape == (batch_size, time_steps, global_obs_dim)
+
+    # Case 2: Test "image" type global_obs_shape
+    global_obs_shape = [3, 64, 64]  # Image-shaped global_obs_shape (C, H, W)
+    qmix_model_image = QMix(agent_num, obs_dim, global_obs_shape, action_dim, [64, 128, embedding_dim], mixer=True)
+
+    # Simulate input for the "image" type global_state
+    C, H, W = global_obs_shape
+    global_state_image = torch.randn(batch_size, time_steps, C, H, W)
+    processed_image = qmix_model_image._process_global_state(global_state_image)
+
+    # Ensure the output shape is correct [batch_size, time_steps, embedding_dim]
+    assert processed_image.shape == (batch_size, time_steps, embedding_dim)

dizoo/petting_zoo/config/ptz_pistonball_qmix_config.py

@@ -0,0 +1,79 @@
+from easydict import EasyDict
+
+n_pistons = 20
+collector_env_num = 8
+evaluator_env_num = 8
+max_env_step = 3e6
+
+main_config = dict(
+    exp_name=f'data_pistonball/ptz_pistonball_n{n_pistons}_qmix_seed0',
+    env=dict(
+        env_family='butterfly',
+        env_id='pistonball_v6',
+        n_pistons=n_pistons,
+        max_cycles=125,
+        agent_obs_only=False,
+        continuous_actions=False,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        stop_value=1e6,
+        manager=dict(shared_memory=False,),
+    ),
+    policy=dict(
+        cuda=True,
+        model=dict(
+            agent_num=n_pistons,
+            obs_shape=(3, 457, 120),  # RGB image observation shape for each piston agent
+            global_obs_shape=(3, 560, 880),  # Global state shape
+            action_shape=3,  # Discrete actions (0, 1, 2)
+            hidden_size_list=[32, 64, 128, 256],
+            mixer=True,
+        ),
+        learn=dict(
+            update_per_collect=20,
+            batch_size=32,
+            learning_rate=0.0001,
+            clip_value=5,
+            target_update_theta=0.001,
+            discount_factor=0.99,
+            double_q=True,
+        ),
+        collect=dict(
+            n_sample=16,
+            unroll_len=5,
+            env_num=collector_env_num,
+        ),
+        eval=dict(env_num=evaluator_env_num),
+        other=dict(
+            eps=dict(
+                type='exp',
+                start=1.0,
+                end=0.05,
+                decay=100000,
+            ),
+            replay_buffer=dict(
+                replay_buffer_size=5000,
+            ),
+        ),
+    ),
+)
+main_config = EasyDict(main_config)
+
+create_config = dict(
+    env=dict(
+        import_names=['dizoo.petting_zoo.envs.petting_zoo_pistonball_env'],
+        type='petting_zoo_pistonball',
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(type='qmix'),
+)
+create_config = EasyDict(create_config)
+
+ptz_pistonball_qmix_config = main_config
+ptz_pistonball_qmix_create_config = create_config
+
+if __name__ == '__main__':
+    # or you can enter `ding -m serial -c ptz_pistonball_qmix_config.py -s 0`
+    from ding.entry import serial_pipeline
+    serial_pipeline((main_config, create_config), seed=0, max_env_step=max_env_step)