Add random initial action in ddpg (#13)

* Add random initial actions in ddpg

* Add reacher-v2 example of ddpg

scripts/algorithms/per/ddpg_agent.py

@@ -75,24 +75,24 @@ class Agent(AbstractAgent):
             self.load_params(args.load_from)
 
         # replay memory
-        self.beta = self.hyper_params["PER_BETA"]
-        self.memory = PrioritizedReplayBuffer(
-            self.hyper_params["BUFFER_SIZE"],
-            self.hyper_params["BATCH_SIZE"],
-            alpha=self.hyper_params["PER_ALPHA"],
-        )
+        if not self.args.test:
+            self.beta = self.hyper_params["PER_BETA"]
+            self.memory = PrioritizedReplayBuffer(
+                self.hyper_params["BUFFER_SIZE"],
+                self.hyper_params["BATCH_SIZE"],
+                alpha=self.hyper_params["PER_ALPHA"],
+            )
 
     def select_action(self, state: np.ndarray) -> torch.Tensor:
         """Select an action from the input space."""
         self.curr_state = state
 
         state = torch.FloatTensor(state).to(device)
+        selected_action = self.actor(state)
 
         if not self.args.test:
-            selected_action = self.actor(state)
             selected_action += torch.FloatTensor(self.noise.sample()).to(device)
-
-        selected_action = torch.clamp(selected_action, -1.0, 1.0)
+            selected_action = torch.clamp(selected_action, -1.0, 1.0)
 
         return selected_action
 
@@ -101,7 +101,8 @@ class Agent(AbstractAgent):
         action = action.detach().cpu().numpy()
         next_state, reward, done, _ = self.env.step(action)
 
-        self.memory.add(self.curr_state, action, reward, next_state, done)
+        if not self.args.test:
+            self.memory.add(self.curr_state, action, reward, next_state, done)
 
         return next_state, reward, done