fromtorch.distributionsimportNormal mu= torch.tensor([1, 10], dtype=torch.float32) sigma= torch.tensor([1], dtype=torch.float32) dist= Normal(mu, sigma)#设置高斯分布的均值和方差dist.sample()#采样>>> tensor([-0.9153, 8
"scale")], out_keys=[env.action_key], distribution_class=TanhNormal, distribution_kwargs={ "low": env.unbatched_action_spec[env.action_key].space.low, "high": env.unbatched_action_spec[env.action_key].space.high, }, return_log_prob=True, log_prob_key=("agents", "sample_log_pro...
在我们环境中多个代理必须在 LIDAR 传感器的引导下导航到各自的目标,同时避免碰撞。 from torchrl.envs.libs.vmas import VmasEnv max_steps = 100num_vmas_envs = frames_per_batch // max_stepsscenario_name = "navigation"n_agents = 3env =...
1linspace_tensor = torch.linspace(0, 1, 5) 其它分布: PyTorch还支持更多复杂的概率分布,例如高斯分布、伯努利分布、指数分布等,可以通过torch.distributions模块来创建相应的分布对象并使用.sample()方法来生成随机数。确保在进行随机实验时,能够控制随机种子以确保可复现性,可以使用 torch.manual_seed(seed) 或 to...
问两个torch.distribution.Distribution对象的KL散度EN为了开始了解熵到底指的是什么，让我们深入了解信息...
empty(2,3).uniform_(5,10).type(torch.FloatTensor) # 方法二: from torch.distributions import uniform distribution = uniform.Uniform(torch.Tensor([0.0]),torch.Tensor([5.0])) distribution.sample(torch.Size([2,3]) 7.9.2 伯努利分布:torch.bernoulli torch.bernoulli(input, out=None)...
target: sample from the Gaussian distribution. var: tensor of positive variance(s), one for each of the expectations in the input (heteroscedastic), or a single one (homoscedastic). full (bool, optional): include the constant term in the loss calculation. Default: ``False``. ...
Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1. torch.randn_like(input) is equivalent to torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device). ...
这是一种重参数化技巧,主要是通过调用rsample()函数来实现的,参数化随机变量可以通过无参数随机变量的参数化确定性函数来构造。参数化以后,采样过程就变得可微分了,也就支持了网络的后向传播。实现如下(PyTorch官方实现): params = policy_network(state) m = Normal(*params) # Any distribution with .has_rsam...
ERROR:Could not find a version that satisfies the requirement torch1.2.0 ERROR:No matching distribution found for torch1.2.0 三、解决方法 关于"Could not find a version that satisfies…"这类问题,网上说的比较多的主要有这么几种方法: 1、更新pip ...