goal conditioned reinforcement learning '目标条件强化学习',指的是一种强化学习的方法,它将目标作为强化学习的重要组成部分。在这种方法中,智能体的行为是被目标所控制的,而不是仅仅是最大化奖励。这种方法可以被应用于各种领域,如机器人控制、游戏玩法、自动驾驶等。该方法的优点是可以使智能体更加高效地完成任务,...
Relabeling by Learning—— 只在过去的经验中挑选 relabel goal 会限制 diversity,所以可以考虑根据某些条件比如当前策略的平均回报来生成 achieved goals Foresight—— 考虑到人类不只从失败中学习,还会规划未来,于是可以利用一个dynamics model,生成虚拟轨迹用于 relabel。 五、Future Prospects Learning Totally Intrinsic...
arXiv:2205.13044v1 [cs.LG] 25 May 2022Near-Optimal Goal-Oriented ReinforcementLearning in Non-Stationary EnvironmentsLiyu ChenUniversity of Southern Californialiyuc@usc.eduHaipeng LuoUniversity of Southern Californiahaipengl@usc.eduAbstractWe initiate the study of dynamic regret minimization for goal-...
Recently, this property of transformer modelshas also been utilized for reinforcement learning (RL) by learning in-context. In in-context learning for decision-making problems, i.e., RL, a transformer model is usually pre-trained on an offline dataset and is tasked to predict the most likely...
while reinforcement learning (RL) has an advantage in handling sequential decision making, it faces challenges in multi-level problems because of the delayed rewards and complex states. Hierarchical reinforcement learning (HRL) is a layered algorithm based on RL. HRL has been proved to be effective...
Abstract: 使用RL训练的agent仅能够实现通过其奖励功能指定的单个任务。这种方法不能很好地适应代理需要执行各种任务的设置,例如导航到房间中的不同位置或将对象移动到不同的位置。相反,我们提出了一种方法,允许代理自动发现它能够在其环境中执行的任务范围。我们使用生
Goal-oriented learning has become a core concept in reinforcement learning (RL), extending the reward signal as a sole way to define tasks. However, as parameterizing value functions with goals increases the learning complexity, efficiently reusing past experience to update estimates towards several ...
Karlsson, J. (1994). Task Decomposition in Reinforcement Learning. In desJardins, M. & Ram, A. (eds.) Proceedings ofThe AAAI Spring Symposium on Goal-Driven Learning, 46–53. Stanford, CA. Kass, A. & Leake, D. B. (1988). Case-Based Reasoning Applied to Constructing Explanations. In...
几篇论文实现代码:《Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning》(ICML 2023) GitHub: github.com/quasimetric-learning/quasimetric-rl 《ExposureDiffusion: Learning to Expos...
《Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning》论文阅读 mattttt 3 人赞同了该文章 论文原文链接如下: 论文原文ieeexplore.ieee.org/document/9645287?source=authoralert 论文开源代码地址: 开源代码github.com/reiniscimurs/DRL-robot-navigation 论文简介 作者提出了一种基于深度强化...