class LSPI_SARSA(Agent):
def __init__(self,representation,policy,domain,logger, lspi_iterations = 5, sample_window = 100, epsilon = 1e-3, re_iterations = 100,initial_alpha =.1, lambda_=0,alpha_decay_mode ='dabney', boyan_N0 = 1000):
self.SARSA = SARSA(representation, policy, domain,logger, initial_alpha, lambda_,alpha_decay_mode, boyan_N0)
self.LSPI = LSPI(representation,policy,domain,logger, lspi_iterations, sample_window, epsilon, re_iterations)
super(LSPI_SARSA,self).__init__(representation,policy,domain,logger)
def learn(self,s,a,r,ns,na,terminal):
self.LSPI.process(s,a,r,ns,na,terminal)
if self.LSPI.samples_count+1 % self.LSPI.steps_between_LSPI == 0:
self.LSPI.representationExpansionLSPI()
if terminal:
self.episodeTerminated()
else:
self.SARSA.learn(s,a,r,ns,na,terminal)
def __init__(self,representation,policy,domain,logger, lspi_iterations = 5, sample_window = 100, epsilon = 1e-3, re_iterations = 100,initial_alpha =.1, lambda_=0,alpha_decay_mode ='dabney', boyan_N0 = 1000):
self.SARSA = SARSA(representation, policy, domain,logger, initial_alpha, lambda_,alpha_decay_mode, boyan_N0)
self.LSPI = LSPI(representation,policy,domain,logger, lspi_iterations, sample_window, epsilon, re_iterations)
super(LSPI_SARSA,self).__init__(representation,policy,domain,logger)
# import sampler # # samples = sampler.sample(50) # print(samples) import LSPI import matplotlib.pyplot as plt import sampler pi,distances = LSPI.run_LSPI() plt.plot(distances) plt.show() sampler.use_policy(pi)