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TestRL.py

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+import numpy as np
+import MDP
+import RL
+
+
+''' Construct simple MDP as described in Lecture 2a Slides 13-14'''
+T = np.array([[[0.5,0.5,0,0],[0,1,0,0],[0.5,0.5,0,0],[0,1,0,0]],[[1,0,0,0],[0.5,0,0,0.5],[0.5,0,0.5,0],[0,0,0.5,0.5]]])
+R = np.array([[0,0,10,10],[0,0,10,10]])
+discount = 0.9        
+mdp = MDP.MDP(T,R,discount)
+rlProblem = RL.RL(mdp, np.random.normal)
+
+# Test Q-learning 
+[Q,policy,_] = rlProblem.qLearning(s0=0,initialQ=np.zeros([mdp.nActions,mdp.nStates]),nEpisodes=1000,nSteps=100,epsilon=0.3)
+print ("\nQ-learning results")
+print (Q)
+print (policy)