def learn_genomeIRL(self): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15 ; eonnIRL.mutate_prob = 0.4 ; eonnIRL.mutate_frac = 0.1;eonnIRL.mutate_std = 0.8;eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, self.percieved_eval,400) # These are imported functions from EONNIRL
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / self.percieved_eval(champion.policy))
#print '\ngenome:\n%s' % champion.genome
return champion.policy
def learnGenomeIRL(theta): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15
eonnIRL.mutate_prob = 0.4
eonnIRL.mutate_frac = 0.1
eonnIRL.mutate_std = 0.8
eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, hoverIRL, theta)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\nerror:', math.exp(1 / hoverIRL(champion.policy, theta))
print '\ngenome:\n%s' % champion.genome
def learnGenomeIRL(theta): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15
eonnIRL.mutate_prob = 0.4
eonnIRL.mutate_frac = 0.1
eonnIRL.mutate_std = 0.8
eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, hoverIRL,theta)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\nerror:', math.exp(1 / hoverIRL(champion.policy,theta))
print '\ngenome:\n%s' % champion.genome
