#sensible consummatory values.
# "approach potential partner" will exhaust the opportunity after one turn.
bb.action_elicitor[i_friends,i_friends]=0
# "meet friends" doesn't really get 'consumed' at all..
bb.action_elicitor[i_partner,i_partner]=1
#studying also doesn't get 'consumed'.
bb.action_elicitor[i_study,i_study]=0
#neither is the need satiated very quickly.
bb.action_state[i_study,i_study]=bb.action_state[i_study,i_study]/4
bb.display_current_state()
while bb.actions[0].value==0:
bb.step_and_display(1,2)
#OK. In order to do copy we're going to have to copy the individual attributes. Otherwise we're currently capturing graph objects and things and that's causing problems.
print("tendency increasing over time:")
print([iter["actions"][0].tendency for iter in bb.record])
bb.step_and_display()
#OK. Now, we introduce a potential partner, friends, and also a study environment
bb.elicitors[i_friends].value=1
bb.elicitors[i_partner].value =1
bb.elicitors[i_study].value =1
bb.step_and_display()
#next task:
#need to set up the relationships.
bb.state_action[range(0,4),:]=np.diag([1.0]*4)
bb.state_action[4,:]=[0.25,0.25,0.25,0.25] #should modify this study mood to be less.
#and the action_elicitor links.
bb.state_action[range(0,4),:]=np.diag([1.0]*4)
bb.state_action[4,:]=[0.25,0.25,0.25,0.25]
#how much does each action satiate the "mood" item?
#this really ought to depend on the amount of reward signal and we don't have a way to do that in the existing model
#am I going down the wrong track?
#not necessarily...but it's not going to entirely make sense until we make the reward signal into an associationist model
#can we do that now or are there other more important things to sort out first?
########################################################################################################################
bb.step_and_display()
#model doesn't do anything: there's no environment elicitation. let's bring some food in.
bb.elicitors[0].value=1.0
bb.step()
bb.display_current_state()
#still nothing...because there's negative association with the action, it's not done.
#I'm not sure about including this reinforcement learning...
#anyhow, let's show the model that eating has a smaller negative than positive value.
bb.actions[0].neg_expectancy=0.5
bb.actions[0].neg_val=0.5
#bb.actions[1].pos_expectancy=2
#approaching a potential partner carries a high risk of rejection
#while meeting friends carries very little negative but also lesser positive expected gain
#partner
