refactoring

[shutbox.git] / src / qtable.py

diff --git a/src/qtable.py b/src/qtable.py
index 5bb1c519f29001855a9b6e2618b6cd7c04663f84..29a3081235bfa8675657cdc6d0b866158ee88f7a 100755 (executable)
--- a/src/qtable.py
+++ b/src/qtable.py
@@ -6,7 +6,7 @@ import sys
 
 from game import Game
 
 
 from game import Game
 

-learning_rate = 1.0
+learning_rate = 0.001

 discount_factor = 1.0
 
 states_dim = 36864 # 2^10 * 6^2
 discount_factor = 1.0
 
 states_dim = 36864 # 2^10 * 6^2


@@ -40,7 +40,6 @@ def select_option(opts, qs):
         decision_pt += qs[ opt_qid_pair[1] ]
         if ran_pt <= decision_pt:
             return (opt_qid_pair[0], opt_qid_pair[1])
         decision_pt += qs[ opt_qid_pair[1] ]
         if ran_pt <= decision_pt:
             return (opt_qid_pair[0], opt_qid_pair[1])

-    return (None, None)

 
 Q = np.ones([states_dim, actions_dim])
 
 
 Q = np.ones([states_dim, actions_dim])
 


@@ -51,19 +50,20 @@ for i in range(num_episodes):
     g.dice()
     state_qid = find_state_qid(g.get_shutable(), g.get_diced())
     while not g.is_over():
     g.dice()
     state_qid = find_state_qid(g.get_shutable(), g.get_diced())
     while not g.is_over():

-        opt, opt_qid = select_option( g.get_options(), Q[state_qid, :] )
-        if opt:
+        options = g.get_options()
+        if len(options) > 0:
+            opt, opt_qid = select_option( options, Q[state_qid, :] )

             old_score = g.get_score()
             g.shut(opt)
             g.dice()
             old_score = g.get_score()
             g.shut(opt)
             g.dice()

-            reward = (g.get_score() - old_score) / 11.0
+            reward = g.get_score() - old_score

             new_state_qid = find_state_qid(g.get_shutable(), g.get_diced())
             Q[state_qid, opt_qid] += \
                 learning_rate * (reward
                                  + discount_factor * np.max(Q[new_state_qid, :])
                                  - Q[state_qid, opt_qid])
             state_qid = new_state_qid
             new_state_qid = find_state_qid(g.get_shutable(), g.get_diced())
             Q[state_qid, opt_qid] += \
                 learning_rate * (reward
                                  + discount_factor * np.max(Q[new_state_qid, :])
                                  - Q[state_qid, opt_qid])
             state_qid = new_state_qid

-    Q[state_qid, :] = 0
+    Q[state_qid, :] = 0.0

     running_score[0] *= 0.99999999
     running_score[0] += g.get_score()
     running_score[1] *= 0.99999999
     running_score[0] *= 0.99999999
     running_score[0] += g.get_score()
     running_score[1] *= 0.99999999