v_f = value_iteration(gamma=0.9, theta=0.01)
graph_vals(v1, 'Sweep 1')
graph_vals(v10, 'Sweep 10')
graph_vals(v_f, 'Optimal V*')
#
# Policy Iteration
#
values = []
V, pi = policy_iteration(gamma=0.9, theta=0.01, value_list=values)
for i, v in enumerate(values):
graph_vals(v, 'Policy Evaluation %d' % (i + 1))
'''
#Gamma Variations
print 'Generating Gamma Variations'
for g in (0.9, 0.5, 0.3):
values = []
V, pi = policy_iteration(theta=0.01, value_list=values, gamma=g)
print 'gamma = %f:' % (g)
for s in ((4, 7, 1), (1, 3, 6), (9, 2, 1)):
print 'state %s => action %s' % (s, pi[s])
'''
for i, v in enumerate(values):
graph_vals(v, 'Policy Evaluation %d. Gamma: %f' % (i + 1, g), 'gamma_%d_%d' % (int(g*10), i+1))
'''
'''
Test value iteration
Created on 24 Sep 2009
@author: joh
'''
from value_methods import policy_iteration
if __name__ == '__main__':
values = []
V, pi = policy_iteration(gamma=0.9, theta=0.01, value_list=values)
assert values[-1] == V
def vcmp(v1, v2):
#print 'vcmp',v1,v2
return cmp(v1[1], v2[1])
V = sorted(V.items(), cmp=vcmp)
for v in V:
print v, "=>", pi[v[0]]
print "%d policy evaluation steps were required." % (len(values))
graph_vals(v1, 'Sweep 1')
graph_vals(v10, 'Sweep 10')
graph_vals(v_f, 'Optimal V*')
#
# Policy Iteration
#
values = []
V, pi = policy_iteration(gamma=0.9, theta=0.01, value_list=values)
for i, v in enumerate(values):
graph_vals(v, 'Policy Evaluation %d' % (i + 1))
'''
#Gamma Variations
print 'Generating Gamma Variations'
for g in(0.9, 0.5, 0.3):
values = []
V, pi = policy_iteration(theta=0.01, value_list=values, gamma=g)
print 'gamma = %f:' % (g)
for s in ((4,7,1), (1,3,6), (9,2,1)):
print 'state %s => action %s' % (s, pi[s])
'''
for i, v in enumerate(values):
graph_vals(v, 'Policy Evaluation %d. Gamma: %f' % (i + 1, g), 'gamma_%d_%d' % (int(g*10), i+1))
'''
