PPO是基于策略的强化学习算法，它属于offpolicy算法。其核心在于通过限制策略梯度更新的幅度来优化策略，这种限制可以通过KL散度或Clip方法来实现。KL penalty方法：在策略更新时，通过添加一个KL散度项作为惩罚，防止新旧策略之间的差异过大。神经网络参数按照包含KL散度惩罚项的特定公式进行更新。Clip
KL penalty 和 Clip PPO算法的核心在于更新策略梯度,主流方法有两种,分别是KL散度做penalty,另一种是Clip剪裁,它们的主要作用都是限制策略梯度更新的幅度,从而推导出不同的神经网络参数更新方式 采用KL penalty算法,那么神经网络参数按照下面的方式更新 采用Clip算法,那么神经网络参数按照下面的方式更新 那么采用KL penalt...
PPO（Proximal Policy Optimization）是基于策略的强化学习算法，属于off-policy算法。核心在于通过KL散度或Clip方法限制策略梯度更新的幅度，从而优化策略。使用KL penalty算法时，神经网络参数按特定公式更新；采用Clip算法时，参数更新方式亦有差异。这两种方法在PPO算法中体现为更新策略的不同方式。伪代码展示...
In addition, per-token probability distributions from the RL policy are compared to the ones from the initial model to compute a penalty on the difference between them. In multiple papers from OpenAI, Anthropic, and DeepMind, this penalty has been designed as a scaled version of the Kullback–...
KUALA LUMPUR (May 28): The decision to drop plans to build the high-speed rail link between Kuala Lumpur and Singapore is final, Prime Minister Tun Dr Mahathir Mohamad said, adding that it could see Malaysia facing up to RM500 million in penalty. Speaki
In addition, per-token probability distributions from the RL policy are compared to the ones from the initial model to compute a penalty on the difference between them. In multiple papers from OpenAI, Anthropic, and DeepMind, this penalty has been designed as a scaled version of the Kullback–...
In addition, per-token probability distributions from the RL policy are compared to the ones from the initial model to compute a penalty on the difference between them. In multiple papers from OpenAI, Anthropic, and DeepMind, this penalty has been designed as a scaled version of the Kullback–...
In addition, per-token probability distributions from the RL policy are compared to the ones from the initial model to compute a penalty on the difference between them. In multiple papers from OpenAI, Anthropic, and DeepMind, this penalty has been designed as a scaled version of the Kullback–...