firs commit for our new MARL algorithms library, contains working implementations of IAC, SNAC and SEAC

algorithms/marl/snac.py

@@ -0,0 +1,32 @@
+from algorithms.marl.base_ac import BaseActorCritic
+import torch
+from torch.distributions import Categorical
+from pathlib import Path
+
+
+class LoopSNAC(BaseActorCritic):
+    def __init__(self, cfg):
+        super().__init__(cfg)
+
+    def load_state_dict(self, path: Path):
+        path2weights = list(path.glob('*.pt'))
+        assert len(path2weights) == 1, f'Expected a single set of weights but got {len(path2weights)}'
+        self.net.load_state_dict(torch.load(path2weights[0]))
+
+    def init_hidden(self):
+        hidden_actor = self.net.init_hidden_actor()
+        hidden_critic = self.net.init_hidden_critic()
+        return dict(hidden_actor=torch.cat([hidden_actor]   * self.n_agents,  0),
+                    hidden_critic=torch.cat([hidden_critic] * self.n_agents,  0)
+                    )
+
+    def get_actions(self, out):
+        actions = Categorical(logits=out['logits']).sample().squeeze()
+        return actions
+
+    def forward(self, observations, actions, hidden_actor, hidden_critic):
+        out = self.net(self._as_torch(observations).unsqueeze(1),
+                       self._as_torch(actions).unsqueeze(1),
+                       hidden_actor, hidden_critic
+                       )
+        return out