To address this, we introduce a deep reinforcement adaptive learning framework designed to train locomotion skills for multiple drones, focusing on zero-shot transfer from simu-lation to real-world environments. our method incorporates specialized reward structures and domain randomization tech-niques tailored for multiple drones, thereby mitiga...

Lecture中第二部分介绍了两种较为经典的IRL算法，第一种是feature matching+maximum margin方法，第二种是maximum entropy IRL的方法。 最经典的方法是feature matching的方法，该方法的思路是将reward function用线性函数表达： r_ {\psi} (\textbf s, \textbf a)=\sum_ {i}\psi_i f_i (\textbf s, \textbf a)=\psi^T \textbf f (\textbf s, \textbf a) \\ 这里的 \textbf f 是从专家数据中获取 …

2015年7月17日 · Abstract: This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem. We show in this context that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient ...

2022年4月13日 · 逆强化学习（Inverse Reinforcement Learning, IRL）是一种机器学习方法，它试图通过观察专家的行为来推断出潜在的奖励函数。在这个“lets-do-irl-master”压缩包中，我们很可能会找到关于如何实现和应用逆强化学习...

To enable the robots to adapt to a more diverse distribution of obstacles, we introduce DRAL, a novel deep reinforcement learning method that leverages adaptive control during the unknown payload transportation task with a continuous action space.

2024年9月5日 · Integrating our DRAL algorithm enables multiple UAVs to learn optimal control strategies that adapt to dynamic conditions and uncertainties. This innovation enhances the robustness and flexibility of indoor navigation and opens new possibilities for complex multi-drone operations in confined spaces.

1983年8月25日 · Ultraviolet irradiation of the DNA substrate at relatively low doses inhibits the activity of Dral by “protecting” the recognition sequence and this may be exploited to give control of partial digestion of DNA by DraI.

2024年9月5日 · Integrating our DRAL algorithm enables multiple UAVs to learn optimal control strategies that adapt to dynamic conditions and uncertainties. This innovation enhances the robustness and...

集成我们的 dral 算法使多个无人机能够学习适应动态条件和不确定性的最佳控制策略。 这项创新增强了室内导航的稳健性和灵活性，并为有限空间内复杂的多无人机操作开辟了新的可能性。

2022年3月21日 · 逆强化学习（Inverse Reinforcement Learning，简称IRL）是强化学习（Reinforcement Learning，简称RL）的一个子领域，它的目标是从专家的行为中学习策略。 在传统的 强化学习 中，智能体通过 与 环境交互，根据奖励信号来学习一个策略。