Similarly, Ma [14] integrated entropy into reward, and log probability into state value function and state–action value functions, to improve both TRPO and PPO. In this study, we refer this kind of entropy-regularized objective TRPOs as ERO-TRPO. To the best of our knowledge, this ...
In this blog post, we’ll break down the training process into three core steps: Pretraining a language model (LM), gathering data and training a reward model, and fine-tuning the LM with reinforcement learning. To start, we'll look at how language models are pretrained. Pretraining ...
The mathematical model of modified fragments is established to describe the process of reinforcement learning, and the change of probability distribution is used to explain the stationarity analysis of reinforcement learning. Through introducing the concept of entropy in information theory, the reward funct...
R-drop: Regularized dropout for neural networks. Adv. Neural Inf. Process. Syst. 2021, 34, 10890–10905. [Google Scholar] Izmailov, P.; Podoprikhin, D.; Garipov, T.; Vetrov, D.; Wilson, A. Averaging weights leads to wider optima and better generalization. arXiv 2018, arXiv:...