Imitation Learning algorithms learn a policy from demonstrations of expert behavior. Somewhat counterintuitively, we show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning, which is commonly considered more difficult. We conduct experiments which confirm ...
Reinforcement Learning with Safety Layer for 17:57 【RLChina论文研讨会】第56期 宋昊霖 MA2CL:Masked Attentive Contrastive Learning for Multi-Age 19:28 【RLChina论文研讨会】第55期 冯熙栋 ChessGPT: Bridging Policy Learning and Language Modeling 34:02 【RLChina论文研讨会】第55期 刘旭辉 How To ...
与监督学习不同,强化学习不需要预先标注的数据,而是通过与环境的互动自主学习。 Reinforcement Learning (source: https://speech.ee.ntu.edu.tw/~hylee/ml/ml2021-course-data/drl_v5.pdf) 强化学习的应用非常广泛,比如: 游戏AI: 训练AI玩各种游戏,例如围棋、Atari游戏等。 机器人控制: 控制机器人完成各种任务...
【RLChina论文研讨会】第93期 耿子介 Reinforcement Learning with Tree Search for Fast Macro Pl, 视频播放量 384、弹幕量 0、点赞数 8、投硬币枚数 1、收藏人数 10、转发人数 0, 视频作者 RLChina强化学习社区, 作者简介 ,相关视频:【RLChina论文研讨会】第95期 庄子文
Learning to reinforcement learn JX Wang 多阶段的学习， 通过学习先验结构， 流行表示，世界模型的交互...
Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to ...
deep-reinforcement-learningperceptionautonomous-drivingimitation-learningcarla-simulator UpdatedFeb 14, 2022 Python Load more… Add a description, image, and links to theimitation-learningtopic page so that developers can more easily learn about it. ...
to achieve. Second, we utilize expert demonstrations within the hierarchical action space to dramatically reduce cost of exploration. Our framework is flexible and can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels of the hierarchy. ...
Reinforcement Learning with Function Approximation Reinforcement learning (RL) is a key problem in the field of Artificial Intelligence. The main goal is for an agent to learn to act in an unknown environment in a way that maximizes a reward that it receives from the environment. As the ......
that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort ...