如果没有指定参数,则默认生成单个样本。此外,Categorical分布还提供了log_prob()方法,用于计算给定样本的对数概率。 在上述基础上,继续要求不重复抽样 如果需要基于给定的概率分布进行不重复采样,可以使用torch.multinomial()函数以及循环来实现。 下面是一个示例: import torch # 创建一个大小为 (1, n) 的一行张量...
有个很好的例子能看出log_prob(action)做了什么事: importtorchimporttorch.nn.functionalasF action_logits=torch.rand(5)action_probs=F.softmax(action_logits,dim=-1)dist=torch.distributions.Categorical(action_probs)action=dist.sample()print(dist.log_prob(action),torch.log(action_probs[action])) 会...
AB_means = torch.vstack( [ A_means, B_means])AB_stdevs = torch.vstack( [ A_stdevs, B_stdevs]) Pytorch混合分布的工作方式是通过在原始的Normal分布上使用3个额外的分布Independent、Categorical和MixtureSameFamily来实现的。从本质上讲,它创建了一个混合,...
blend_weight = torch.distributions.Categorical( torch.nn.functional.relu( self.blend_weight)) comp = torch.distributions.Independent(torch.distributions.Normal( self.means, torch.abs( self.stdevs)), 1) gmm = torch.distributions.MixtureSameFamily( blend_weight, comp) return gmm.log_prob(x) def...
m = Categorical(probs) #分类概率 action = m.sample() #采样一个action next_state, reward = env.step(action) #这里为了简化考虑,一个episode只有一个action loss = -m.log_prob(action) * reward #m.log_prob(action) 就是 logp #reward就是前面的r ...
probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() Pathwise derivative 实现这些随机/策略梯度的另一种方...
现在的代码如下：probs = policy_network(state)# NOTE: categorical is equivalent to what used to be called multinomialm = torch.distributions.Categorical(probs)action = m.sample()next_state, reward = env.step(action)loss = -m.log_prob(action) * rewardloss.backward()新的功能 1、目前，有些...
action_dist = torch.distributions.Categorical(action_probs) action = action_dist.sample() log_prob = action_dist.log_prob(action) log_probs.append(log_prob) # 执行行动并获得奖励 obs, reward, done, _ = env.step(action.item())
(self, x):blend_weight = torch.distributions.Categorical( torch.nn.functional.relu(self.blend_weight))comp = torch.distributions.Independent(torch.distributions.Normal(self.means, torch.abs(self.stdevs)),1)gmm = torch.distributions.MixtureSameFamily( blend_weight, comp)returngmm.log_prob(x)def...
# 1.将pi列表的权重转化为概率构建离散分布dist = torch.distributions.Categorical(logits=pi)dist.sample()# 按概率抽样下标dist.log_prob(action_batch)# 对action_batch中每个元素重构成仅1个索引值为1的独热编码序列,对每个序列计算交叉熵 # 2.构建(μ,σ)正态分布dist = torch.distributions.Normal# 构建...