提出circle loss。 过去,针对class-level label,默认用softmax-CrossEntropy计算损失;针对pairswise-level label,默认用triplet loss计算损失。 现在,针对这两种情况,都可以用一种损失函数,即circle loss。实践表明,circle loss的效果比上面两者都秀。 circle loss API ...
4.Cut-Cross-Entropy 反向算法 在2.1时我们计算了LSE形式下Cross Entropy loss的求导,LSE的导数即是S=\text{softmax}(EC), 2. 计算梯度时,需要重计算\text{softmax}. 考虑已有第325行红色框forward所计算的 LSE, 那么我们可以通过以下推导重新计算出概率对数,再求exp, 即算法里的蓝色框331-334, 而A可...
action_scores_t = net(state_t) loss_t = objective(action_scores_t, acts_t) loss_t.backward() optimizer.step() iter_no += 1 batch = [] 监控agent进度 print(“%d: loss=%.3f, reward_mean=%.3f” % (iter_no, loss_t.item(), reward_mean)) 通过更好的神经网络改进Agent 考虑具有更...
If we are thrown with some loss functions used in supervised ML, they would “make sense” immediately. But it takes some more efforts to understand where it comes from. For example, the good old mean square loss intuitively make sense: it just minimizes the distance between the prediction...
Wouldn't it be great if we use human feedback for generated text as a measure of performance or go even one step further and use that feedback as a loss to optimize the model? That's the idea of Reinforcement Learning from Human Feedback (RLHF); use methods from reinforcement learning...
– 12 – 1 (PpO1O2Pp)O3O4 ∼ 2 1 O1(PpO2O3Pp)O4 ∼ 2 Pp p 4 ∼ O1 O2 O3 O4 3 Pp p 4 ∼ O1 O2 O3 O4 3 Figure 2. Inserting projection operators Pp onto the Verma module of a primary Op into a correlator yields the conformal block with internal weight hp. Which ...
In the optimisation process of the PPO algorithm, an entropy regularity term is introduced in the loss function. The entropy regularity term will encourage the strategy to perform more exploration in situations with high uncertainty. This helps the algorithm to better explore the environment and learn...
The total loss of CPPO algorithm proposed in this paper is lower than that of PPO algorithm. Table 1 is a comparison of the total rewards obtained by CPPO algorithm and PPO algorithm in the same number of iterations. As it can be seen, the total rewards of CPPO algorithm are 226,214, ...
The whole DDPO algorithm is pretty much the same as Proximal Policy Optimization (PPO) but as a side, the portion that stands out as highly customized is the trajectory collection portion of PPO Here’s a diagram to summarize the flow: DDPO and RLHF: a mix to enforce aestheticness The ...
1.3 (aim) maximum entropy rl framework: maximize expected reward and entropy 1.4 (past works) PPO, TRPO, A3C, DDPG 2. the insights of the method 2.1 maximum entropy rl objective: J(π)=∑t=0TE(st,at)∼ρπ[r(st,at)+αH(π(⋅|st))] where α is the temperature parameter ...