Recently, model-based reinforcement learning algorithms have demonstrated
remarkable efficacy in visual input environments. These approaches begin by
constructing a parameterized simulation world model of the real environment
through self-supervised learning. By leveraging the imagination of the world
model, the agent's policy is enhanced without the constraints of sampling from
the real environment. The performance of these algorithms heavily relies on the
sequence modeling and generation capabilities of the world model. However,
constructing a perfectly accurate model of a complex unknown environment is
nearly impossible. Discrepancies between the model and reality may cause the
agent to pursue virtual goals, resulting in subpar performance in the real
environment. Introducing random noise into model-based reinforcement learning
has been proven beneficial. In this work, we introduce Stochastic
Transformer-based wORld Model (STORM), an efficient world model architecture
that combines the strong sequence modeling and generation capabilities of
Transformers with the stochastic nature of variational autoencoders. STORM
achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark,
setting a new record among state-of-the-art methods that do not employ
lookahead search techniques. Moreover, training an agent with $1.85$ hours of
real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics
card requires only $4.3$ hours, showcasing improved efficiency compared to
previous methodologies.

近年来，基于模型的强化学习算法在视觉输入环境中展现出了显著的效果。本研究介绍了一种名为 STORM 的高效世界模型架构，它将 Transformer 的强大序列建模和生成能力与变分自编码器的随机性结合起来，取得了 Atari 100k 基准测试的均值人类表现为 126.7％的成绩，同时在使用单个 NVIDIA GeForce RTX 3090 显卡进行 1.85 小时的实时交互训练时仅需 4.3 小时，证明其相对于之前的方法具有更高的效率。

STORM: 基于随机 Transformer 的高效强化学习世界模型

STORM: Efficient Stochastic Transformer based World Models for  Reinforcement Learning

This paper presents CG-Eval, the first comprehensive evaluation of the
generation capabilities of large Chinese language models across a wide range of
academic disciplines. The models' performance was assessed based on their
ability to generate accurate and relevant responses to different types of
questions in six disciplines, namely, Science and Engineering, Humanities and
Social Sciences, Mathematical Calculations, Medical Practitioner Qualification
Examination, Judicial Examination, and Certified Public Accountant Examination.
This paper also presents Gscore, a composite index derived from the weighted
sum of multiple metrics to measure the quality of model's generation against a
reference. The test data and test results can be found at
this http URL

CG-Eval 是首个综合评估大型中文语言模型在广泛学科领域中生成能力的研究，以多个指标的加权和为基础派生出 Gscore 综合指数，用于评估模型生成的质量。