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src/a/i/AIQ-HEAD/agents/Q_l.py   AIQ(Download)
 
        # find an optimal action according to current Q values
        opt_action = self.random_optimal( Q[nstate] )
 
        # action selection

src/a/i/AIQ-HEAD/agents/HLQ_l.py   AIQ(Download)
 
        # find an optimal action according to current Q values
        opt_action = self.random_optimal( Q[nstate] )
 
        # action selection

src/a/i/AIQ-HEAD/agents/Freq.py   AIQ(Download)
 
        # find an optimal action according to mean reward for each action
        opt_action = self.random_optimal( Total/Acts )
 
        # action selection