added individual eps-greedy for VDN

algorithms/vdn_learner.py

@@ -1,4 +1,6 @@
+from typing import Union
 import torch
+import numpy as np
 from algorithms.q_learner import QLearner
 
 
@@ -7,6 +9,21 @@ class VDNLearner(QLearner):
         super(VDNLearner, self).__init__(*args, **kwargs)
         assert self.n_agents >= 2, 'VDN requires more than one agent, use QLearner instead'
 
+    def get_action(self, obs) -> Union[int, np.ndarray]:
+        o = torch.from_numpy(obs).unsqueeze(0) if self.n_agents <= 1 else torch.from_numpy(obs)
+        eps = np.random.rand(self.n_agents)
+        greedy = eps > self.eps
+        agent_actions = None
+        actions = []
+        for i in range(self.n_agents):
+            if greedy[i]:
+                if agent_actions is None: agent_actions = self.q_net.act(o.float())
+                action = agent_actions[i]
+            else:
+                action = self.env.action_space.sample()
+            actions.append(action)
+        return np.array(actions)
+
     def train(self):
         if len(self.buffer) < self.batch_size: return
         for _ in range(self.n_grad_steps):