def fig4(params):
'''
画复杂度曲面
:param params:
:return:
'''
force.init()
ForceGenerator.draw_complex(sigma=1.01)
def run(count=10, **kwargs):
force.init()
for i in range(count):
kwargs['xh'] = i
_run(**kwargs)
env = SingleCartPoleEnv().unwrapped
RL = DeepQNetwork(n_actions=env.action_space.n,
n_features=env.observation_space.shape[0])
force.force_generator = force.ForceGenerator(0.0, 0.0, 0.0, 1.01)
def runa(params):
'''
执行试验A的各个算法
:param params:
:return:
'''
name = params['alg'].strip()
force.init()
if name == 'neat':
experiment_a_neat.run(**params)
elif name == 'hyperneat':
experiment_a_hyperneat.run(**params)
elif name == 'dqn':
experiment_a_dqn.run(10, **params)
elif name == 'ddqn':
experiment_a_ddqn.run(10, **params)
elif name == 'policy':
experiment_a_policy.run(10, **params)
def run(count=10, **kwargs):
force.init()
for i in range(count):
kwargs = {
'mode': 'reset',
'xh': i,
'maxepochcount': 1500,
'complexunit': 100.0
}
env = SingleCartPoleEnv().unwrapped
net = PolicyGradients()
while 1:
result = net.run(**kwargs)
if kwargs['mode'] == 'noreset':
break
if not result:
break
env = SingleCartPoleEnv().unwrapped
net = PolicyGradients()
force.force_generator = force.ForceGenerator(0.0, 0.0, 0.0, 1.01)
changed, newcomplex, k, w, f, sigma = force.force_generator.promptComplex(complexunit)
if not changed or newcomplex is None or newcomplex == complexes_list[-1]:
sess.close()
return False
break # 复杂度已经达到最大,结束
print('新的环境复杂度=%.3f,k=%.2f,w=%.2f,f=%.2f,sigma=%.2f' % (newcomplex, k, w, f, sigma))
if mode == 'reset':
sess.close()
return True
episode_number = 0
sess.close()
if __name__ == '__main__':
force.init()
for i in range(10):
kwargs = {'mode': 'reset', 'xh':i,'maxepochcount' : 1500,'complexunit':100.0}
env = SingleCartPoleEnv().unwrapped
net = PolicyGradients()
while 1:
result = net.run(**kwargs)
if kwargs['mode'] == 'noreset':
break
if not result:
break
env = SingleCartPoleEnv().unwrapped
net = PolicyGradients()
force.force_generator = force.ForceGenerator(0.0, 0.0, 0.0, 1.01)
