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...
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...
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 #这里用负号是因为强化学习是梯度上升 loss.bac...
def get_action(policy: Categorical) -> tuple[int, float]: """Sample an action from the policy Args: policy (Categorical): Policy Returns: tuple[int, float]: Tuple of the action and it's log probability """ action = policy.sample() # Unit tensor # Converts to an int, as this is...
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...
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,基于给定Categorical分布的概率权重。因为我们的新均值和标准集有一个额外的轴,这个轴被用作独立的轴,需要决定从中得出哪个均值/标准集的值。
action_index_local = Categorical(logits=logits).sample() prob_matrix = F.softmax(logits, dim=1) log_prob_matrix = F.log_softmax(logits, dim=1) 调用了pytorch.distributions的Categorical方法,但这部分并未在官网文档中给出(可能是未更新) 请问一下,这三行代码该怎么使用mindspore的方法进行呢?che...
Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() 新的功能 1、目前,有些损失函数能直接计算 mini-batch 中每个 sample 的损失值。 2、构建了一个 in-built Profiler,能对模型进行瓶颈分析,这个 Profiler 同时支持 ...
Categorical.log_prob() applied to continuous-valued tensors (only {0,1}-valued tensors are supported). Such models should be fixed but the previous behavior can be recovered by disabling argument validation using the methods mentioned above. Prohibit assignment to a sparse tensor (#50040) ...
log_prob(x)用来计算输入数据x在分布中的对于概率密度的对数 x = torch.tensor([1, 10, 10, 1], dtype=torch.float32).reshape(-1, 2) dist.log_prob(x) >>> tensor([[ -0.9189, -0.9189], [-41.4189, -41.4189]]) 1. 2. 3. 4. ...