def lspi(maxiter, epsilon, samples, basis, discount, initial_policy):
"""
Runs the LSPI algorithm
"""
iteration = -1
distance = float('inf')
policy = initial_policy
all_policies = [initial_policy]
while (iteration < maxiter) and (distance > epsilon):
# print the number of iterations
iteration = iteration + 1
print ('============================')
print 'LSPI iteration: %i' % iteration
if iteration == 0:
firsttime = 1
else:
firsttime = 0
policy = Policy(policy=policy)
policy.weights = lstdq(samples, all_policies[iteration], policy)[0]
diff = policy.weights - all_policies[iteration].weights
LMAXnorm = LA.norm(diff, np.inf)
L2norm = LA.norm(diff)
distance = L2norm
all_policies.append(policy)
print '================================'
if distance > epsilon:
print 'LSPI finished in %i iterations WITHOUT convergence to a fixed point' % iteration
else:
print 'LSPI converged in %i iterations' % iteration
print
print 'weights'
print policy.weights
print
return policy, all_policies
