另一方面,不同的部分轨迹可能产生同一个z,但是若最优动作是存在冲突的,那么优化(1)作为目标在梯度由\pi传向\phi的过程中会使产生同一个z的情况被消除,故而最终优化的子空间\mathcal{T}'中存在的dynamic model一定与\tau_{0:i}吻合,也从而实现了缩减策略集合 Practical Implementation of Offline Model-based ...
COMBO: Conservative Offline Model-Based Policy Optimization, Yu et al, 2021.NIPS.Algorithm: COMBO. Offline Model-based Adaptable Policy Learning, Chen et al, 2021.NIPS.Algorithm: MAPLE. Online and Offline Reinforcement Learning by Planning with a Learned Model, Schrittwieser et al, 2021.NIPS.Algo...
Offline Model-based Adaptable Policy LearningXiong-Hui ChenYang YuQingyang LiFan-Ming LuoZhiwei QinWenjie ShangJieping YeNeural Information Processing Systems
[5] Xiong-Hui Chen, et al. Offline model-based adaptable policy learning. NeurIPS 2021.[6] Yi...
The Official Code for "MAPLE: Offline Model-based Adaptable Policy Learning". After being accepted in NeurIPS'21, we conducted experiments in NeoRL. The results can be found in the following table. * In this process, we introduced parts of implementation tricks in the NeoRL version of MOPO...
【RLChina论文研讨会】第7期 陈雄辉 Offline Model-based Adaptable Policy Learning 1642 1 1:47:09 App 【RLChina 2020】第7讲 Learning with Sparse Rewards 666 -- 25:48 App 【RLChina 论文研讨会】第4期 王润东 Deep Stock Trading- A Hierarchical RL Framework for Portf 1427 1 25:16 App 【RLChin...
【RLChina论文研讨会】第7期 陈雄辉 Offline Model-based Adaptable Policy Learning 20:08 【RLChina论文研讨会】第7期 倪飞 A Multi-Graph Attributed Reinforcement Learning based Optim 21:48 【RLChina论文研讨会】第7期 马亿 A Hierarchical Reinforcement Learning Based Optimization Fr 22:11 【RLChina论文...
Offline Model-based Adaptable Policy Learning Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei Tony Qin, Shang Wenjie, and Jieping Ye. NeurIPS, 2021. COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Le...
learning is broadly adaptable. At the point when we consider learning on the web, it’s typically the nonconcurrent exercises, for example, introductions and tests that come into view first. There is another significant component to web-based learning, however, and that is the live, coordinated...
MAPLE-Offline Model-based Adaptable Policy Learning for Decision-making in Out-of-Support Regions Motivation offline model-based RL算法会受到model OOD的影响(model在有限的数据集上过拟合,在测试时会产生外推误差)。本文没有将策略探索约束在in-support的区域,而是直接探究在out-of-support区域的行为决策能力,...