documentation: api: learner: matd3-mfq1

docs/source/documents/api/learners/marl/matd3.rst

@@ -1,6 +1,185 @@
 MATD3_Learner
 =====================================
 
+xxxxxx.
+
+.. raw:: html
+
+    <br><hr>
+
+**PyTorch:**
+
+.. py:class::
+  xuance.torch.learners.multi_agent_rl.matd3_learner.MATD3_Learner(config, policy, optimizer, scheduler, device, model_dir, gamma, sync_frequency, delay)
+
+  :param config: xxxxxx.
+  :type config: xxxxxx
+  :param policy: xxxxxx.
+  :type policy: xxxxxx
+  :param optimizer: xxxxxx.
+  :type optimizer: xxxxxx
+  :param scheduler: xxxxxx.
+  :type scheduler: xxxxxx
+  :param device: xxxxxx.
+  :type device: xxxxxx
+  :param model_dir: xxxxxx.
+  :type model_dir: xxxxxx
+  :param gamma: xxxxxx.
+  :type gamma: xxxxxx
+  :param sync_frequency: xxxxxx.
+  :type sync_frequency: xxxxxx
+  :param delay: xxxxxx.
+  :type delay: xxxxxx
+
+.. py:function::
+  xuance.torch.learners.multi_agent_rl.matd3_learner.MATD3_Learner.update(sample)
+
+  xxxxxx.
+
+  :param sample: xxxxxx.
+  :type sample: xxxxxx
+  :return: xxxxxx.
+  :rtype: xxxxxx
+
+.. raw:: html
+
+    <br><hr>
+
+**TensorFlow:**
+
+.. raw:: html
+
+    <br><hr>
+
+**MindSpore:**
+
+.. raw:: html
+
+    <br><hr>
+
+Source Code
+-----------------
+
+.. tabs::
+
+  .. group-tab:: PyTorch
+
+    .. code-block:: python
+
+    """
+    Multi-Agent TD3
+    """
+    from xuance.torch.learners import *
+
+
+    class MATD3_Learner(LearnerMAS):
+        def __init__(self,
+                     config: Namespace,
+                     policy: nn.Module,
+                     optimizer: Sequence[torch.optim.Optimizer],
+                     scheduler: Sequence[torch.optim.lr_scheduler._LRScheduler] = None,
+                     device: Optional[Union[int, str, torch.device]] = None,
+                     model_dir: str = "./",
+                     gamma: float = 0.99,
+                     sync_frequency: int = 100,
+                     delay: int = 3
+                     ):
+            self.gamma = gamma
+            self.tau = config.tau
+            self.delay = delay
+            self.sync_frequency = sync_frequency
+            self.mse_loss = nn.MSELoss()
+            super(MATD3_Learner, self).__init__(config, policy, optimizer, scheduler, device, model_dir)
+            self.optimizer = {
+                'actor': optimizer[0],
+                'critic_A': optimizer[1],
+                'critic_B': optimizer[2]
+            }
+            self.scheduler = {
+                'actor': scheduler[0],
+                'critic_A': scheduler[1],
+                'critic_B': scheduler[2]
+            }
+
+        def update(self, sample):
+            self.iterations += 1
+            obs = torch.Tensor(sample['obs']).to(self.device)
+            actions = torch.Tensor(sample['actions']).to(self.device)
+            obs_next = torch.Tensor(sample['obs_next']).to(self.device)
+            rewards = torch.Tensor(sample['rewards']).to(self.device)
+            terminals = torch.Tensor(sample['terminals']).float().reshape(-1, self.n_agents, 1).to(self.device)
+            agent_mask = torch.Tensor(sample['agent_mask']).float().reshape(-1, self.n_agents, 1).to(self.device)
+            IDs = torch.eye(self.n_agents).unsqueeze(0).expand(self.args.batch_size, -1, -1).to(self.device)
+
+            # train critic
+            _, action_q = self.policy.Qaction(obs, actions, IDs)
+            actions_next = self.policy.target_actor(obs_next, IDs)
+            _, target_q = self.policy.Qtarget(obs_next, actions_next, IDs)
+            q_target = rewards + (1 - terminals) * self.args.gamma * target_q
+            td_error = (action_q - q_target.detach()) * agent_mask
+            loss_c = (td_error ** 2).sum() / agent_mask.sum()
+            # loss_c = F.mse_loss(torch.tile(q_target.detach(), (1, 2)), action_q)
+            self.optimizer['critic_B'].zero_grad()
+            self.optimizer['critic_A'].zero_grad()
+            loss_c.backward()
+            torch.nn.utils.clip_grad_norm_(self.policy.parameters_critic, self.args.grad_clip_norm)
+            self.optimizer['critic_A'].step()
+            self.optimizer['critic_B'].step()
+            if self.scheduler['critic_A'] is not None:
+                self.scheduler['critic_A'].step()
+                self.scheduler['critic_B'].step()
+
+            # actor update
+            if self.iterations % self.delay == 0:
+                _, actions_eval = self.policy(obs, IDs)
+                _, policy_q = self.policy.Qpolicy(obs, actions_eval, IDs)
+                p_loss = -policy_q.mean()
+                self.optimizer['actor'].zero_grad()
+                p_loss.backward()
+                self.optimizer['actor'].step()
+                if self.scheduler is not None:
+                    self.scheduler['actor'].step()
+                self.policy.soft_update(self.tau)
+
+            lr_a = self.optimizer['actor'].state_dict()['param_groups'][0]['lr']
+            lr_c_A = self.optimizer['critic_A'].state_dict()['param_groups'][0]['lr']
+            lr_c_B = self.optimizer['critic_B'].state_dict()['param_groups'][0]['lr']
+
+            info = {
+                "learning_rate_actor": lr_a,
+                "learning_rate_critic_A": lr_c_A,
+                "learning_rate_critic_B": lr_c_B,
+                "loss_critic_A": loss_c.item(),
+                "loss_critic_B": loss_c.item()
+            }
+            if self.iterations % self.delay == 0:
+                info["loss_actor"] = p_loss.item()
+
+            return info
+
+
+
+
+
+
+
+
+
+
+
+
+
+  .. group-tab:: TensorFlow
+
+    .. code-block:: python
+
+
+  .. group-tab:: MindSpore
+
+    .. code-block:: python
+
+
+
 .. raw:: html
 
     <br><hr>
@@ -29,19 +208,18 @@ Source Code
 -----------------
 
 .. tabs::
-  
+
     .. group-tab:: PyTorch
-
-        .. code-block:: python3
 
+        .. code-block:: python3
 
 
     .. group-tab:: TensorFlow
-
-        .. code-block:: python3
 
+        .. code-block:: python3
 
 
     .. group-tab:: MindSpore
 
         .. code-block:: python3
+