We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …

Task-agnostic cross-domain pre-training shows great potential in image-based
Reinforcement Learning (RL) but poses a big challenge. In this paper, we propose …

作为高级别自动驾驶的下一代技术方向, 类脑学习以深度神经网络为策略载体,
以强化学习为训练手段, 通过与环境的交互探索实现策略的自我进化, 最终获得从环境状态到 …

Enhancing state representations can effectively mitigate the issue of low sample efficiency in
reinforcement learning (RL) within high-dimensional input environments. Existing methods …

Dual-arm robots offer enhanced versatility and efficiency over single-arm counterparts by
enabling concurrent manipulation of multiple objects or cooperative execution of tasks using …

Deep reinforcement learning (RL) is a powerful approach for solving optimal control
problems. However, RL-trained policies often suffer from the action fluctuation problem …

The multi-lane roundabout poses significant challenges for autonomous driving due to its
complex road structure and traffic conditions. To address these challenges, this paper …

In recent years, the increasing production and sales of automobiles have led to a notable
rise in congestion on urban road traffic systems, particularly at ramps and intersections with …

Agent-agnostic reinforcement learning aims to learn a universal control policy that can
simultaneously control a set of robots with different morphologies. Recent studies have …

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