policies.py 文件源码

def forward(self, x, *args, **kwargs):
        action = super(DiscretePolicy, self).forward(x, *args, **kwargs)
        probs = F.softmax(action.raw)
        action.value = probs.multinomial().detach()
        action.prob = lambda: probs.t()[action.value[:, 0]].mean(1)
        action.compute_log_prob = lambda a: F.log_softmax(action.raw).t()[a[:, 0]].mean(1)
        action.log_prob = action.compute_log_prob(action.value)
        action.entropy = -(action.prob() * action.log_prob)
        return action