Implement dyna-q to finish project 7

README.md

@@ -38,6 +38,6 @@ unzip -n zips/*.zip -d ./
 - [Report 3](./assess_learners/assess_learners.md)
 - No reports for projects 4 (defeat learners) and 5 (marketsim)
 - [Report 6](./manual_strategy/manual_strategy.md)
-- [Report 7](#)
+- No report for project 7
 - [Report 8](#)
 

qlearning_robot/QLearner.py

@@ -22,8 +22,8 @@ GT honor code violation.
 -----do not edit anything above this line---
 """
 
+import random
 import numpy as np
-import random as rand
 
 
 class QLearner(object):
@@ -49,27 +49,32 @@ class QLearner(object):
         self.radr = radr
         self.dyna = dyna
 
+        if self.dyna > 0:
+            self.model = {}
+            self.state_action_list = []
+
         # self.q = np.random.random((num_states, num_actions))
         self.q = np.zeros((num_states, num_actions))
 
     def _get_a(self, s):
         """Get best action for state. Considers rar."""
-        if rand.random() < self.rar:
-            a = rand.randint(0, self.num_actions - 1)
+        if random.random() < self.rar:
+            a = random.randint(0, self.num_actions - 1)
         else:
             a = np.argmax(self.q[s])
         return a
 
-    def _update_q(self, s, a, s_prime, r):
+    def _update_q(self, s, a, r, s_prime):
         """Updates the Q table."""
         q_old = self.q[s][a]
+        alpha = self.alpha
 
         # estimate optimal future value
         a_max = np.argmax(self.q[s_prime])
         q_future = self.q[s_prime][a_max]
 
         # calculate new value and update table
-        q_new = q_old + self.alpha * (r + self.gamma * q_future - q_old)
+        q_new = (1 - alpha) * q_old + alpha * (r + self.gamma * q_future)
         self.q[s][a] = q_new
 
         if self.verbose:
@@ -95,21 +100,39 @@ class QLearner(object):
         @param r: The reward
         @returns: The selected action
         """
-        self._update_q(self.s, self.a, s_prime, r)
-        self.a = self._get_a(s_prime)
-        self.s = s_prime
-        if self.verbose:
-            print(f"s = {s_prime}, a = {self.a}, r={r}")
+        self._update_q(self.s, self.a, r, s_prime)
+        a = self._get_a(s_prime)
+
         # Update random action rate
         self.rar = self.rar * self.radr
+
+        if self.dyna > 0:
+            self._update_model(self.s, self.a, r, s_prime)
+            self._dyna_q()
+
+        self.a = a
+        self.s = s_prime
         return self.a
 
+    def _update_model(self, s, a, r, s_prime):
+        state_action = (s, a)
+        if not state_action in self.model:
+            self.model[state_action] = (r, s_prime)
+            self.state_action_list.append(state_action)
+
+    def _dyna_q(self):
+        for _ in range(self.dyna):
+            s, a = random.choice(self.state_action_list)
+            r, s_prime = self.model[(s, a)]
+            self._update_q(s, a, r, s_prime)
+
     def author(self):
         return 'felixm'
 
 
 if __name__ == "__main__":
-    q = QLearner(verbose=True)
-    print(q.querysetstate(2))
+    q = QLearner(verbose=True, dyna=2)
+    q.querysetstate(2)
     q.query(15, 1.00)
-    print(q.querysetstate(15))
+    q.querysetstate(15)
+    q.query(17, 0.10)

qlearning_robot/testqlearner.py

@@ -171,7 +171,7 @@ def test(map, epochs, learner, verbose):
 # run the code to test a learner
 def test_code():
 
-    verbose = True # print lots of debug stuff if True
+    verbose = False # print lots of debug stuff if True
 
     # read in the map
     filename = 'testworlds/world01.csv'