In recent years, domains such as natural language processing and image recognition have
popularized the paradigm of using large datasets to pretrain representations that can be …

While bisimulation-based approaches hold promise for learning robust state representations
for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up to …

Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate agent's
knowledge about the underlying dynamics of the environment, enabling learning a world …

Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel
tasks while solely relying on a static dataset. For precise and efficient task identification …

Recently, various pre-training methods have been introduced in vision-based
Reinforcement Learning (RL). However, their generalization ability remains unclear due to …

Training offline RL models using visual inputs poses two significant challenges, ie, the
overfitting problem in representation learning and the overestimation bias for expected …

Learning to control an agent from offline data collected in a rich pixel-based visual
observation space is vital for real-world applications of reinforcement learning (RL). A major …

Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective
agent policy without online interaction, which imposes proper conservative constraints to …

**化学**(Reinforcement learning, RL) 通过智能体与环境在线交互来学**最优策略,
**年来已成为解决复杂环境下感知决策问题的重要手段. 然而, 在线收集数据的方式可能会引发 …

The combination of deep learning and internet-scale data with supervised learning has led
to impressive progress in recent years. However, the potential of this progress has yet to be …