def get_action(self, o_t):
if self.model is None:
raise RuntimeError('model was not initialized with the update_model method')
H = self.H
Ua, Sa, Va, wa, w_ter = self.model
self.betas[0,:] = o_t
### Initialize the expected rewards for the plan ###
### Initialize to the immediate reward function ###
self.alphas[:,:] = self.random_stream.normal(0,1e-1, self.alphas.shape)
self.alphas[-1,:] = w_ter
self.plan_param[:,:] = 0.0
self.stoch_plan, plan_val, values, alphas, betas = c_gradient_improve_plan(self.plan_param,
self.alphas,
self.betas,
self.model,
self.H,
self.discount,
learn_rate = self.learn_rate)
print plan_val
a = self.discrete_actions[sampled_argmax(self.stoch_plan[0], self.random_stream)]
return a
def get_action(self, o_t):
if self.embedded_models is None:
raise RuntimeError('model was not initialized with the update_model method')
H = self.H
Kab, Da, wa, Ua, Va, imp_a, w_ter = self.embedded_models
self.betas[0] = np.tensordot(Va, o_t, (1,0))
### Initialize the expected rewards for the plan ###
### Initialize to the immediate reward function ###
self.alphas[:,:,:] = wa[None,:,:]
self.alphas[-1,:,:] += w_ter
self.alphas *= Da[None,:,:]
self.stoch_plan[:,:] = 1.0/self.stoch_plan.shape[1]
self.stoch_plan, plan_val, alphas, betas, _ = lr_improve_plan(self.stoch_plan,
self.alphas,
self.betas,
self.embedded_models,
self.H,
self.discount,
start_temp = self.start_temp)
a = self.discrete_actions[sampled_argmax(self.stoch_plan[0], self.random_stream)]
return a
def get_action(self, o_t):
if o_t.ndim > 1:
return np.vstack(( self.get_action(o) for o in o_t ))
else:
qa = self.evaluate_actions(o_t)
return self.discrete_actions[sampled_argmax(qa, self.random_stream)]
def get_action(self, o_t):
if self.lem is None:
raise RuntimeError('model was not initialized with the update_model method')
qa = lr_evaluate_actions(o_t, self.lem, self.thetas)
a = self.discrete_actions[sampled_argmax(qa, self.random_stream)]
return a
def get_action(self, o_t):
stoch_plan, plan_val, alphas, betas = self.plan(o_t)
a = self.discrete_actions[sampled_argmax(stoch_plan[0], self.random_stream)]
return a