update

codes/MonteCarlo/agent.py

@@ -16,11 +16,11 @@ import torch
 class FisrtVisitMC:
     ''' On-Policy First-Visit MC Control
     '''
-    def __init__(self,n_actions,cfg):
-        self.n_actions = n_actions
+    def __init__(self,action_dim,cfg):
+        self.action_dim = action_dim
         self.epsilon = cfg.epsilon
         self.gamma = cfg.gamma 
-        self.Q = defaultdict(lambda: np.zeros(n_actions))
+        self.Q = defaultdict(lambda: np.zeros(action_dim))
         self.returns_sum = defaultdict(float) # sum of returns
         self.returns_count = defaultdict(float)
         
@@ -28,11 +28,11 @@ class FisrtVisitMC:
         ''' e-greed policy '''
         if state in self.Q.keys():
             best_action = np.argmax(self.Q[state])
-            action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
+            action_probs = np.ones(self.action_dim, dtype=float) * self.epsilon / self.action_dim
             action_probs[best_action] += (1.0 - self.epsilon)
             action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
         else:
-            action = np.random.randint(0,self.n_actions)
+            action = np.random.randint(0,self.action_dim)
         return action
     def update(self,one_ep_transition):
         # Find all (state, action) pairs we've visited in this one_ep_transition

codes/MonteCarlo/main.py

@@ -79,8 +79,8 @@ def mc_train(cfg,env,agent):
 if __name__ == "__main__":
     mc_cfg = MCConfig()
     env = RacetrackEnv()
-    n_actions=9
-    agent = FisrtVisitMC(n_actions,mc_cfg)
+    action_dim=9
+    agent = FisrtVisitMC(action_dim,mc_cfg)
     rewards,ma_rewards= mc_train(mc_cfg,env,agent)
     save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
     plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)