Offline Model-Based Adaptable Policy Learning 本文将对dynamic model的约束放宽,调研直接在out-of-support的region如何完成决策 Decision-making in Out-of-support Regions 本文将真实的model记为 ρ ,拟合真实模型的imaginary model记为 ρ′ 。本文
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
The Official Code for "MAPLE: Offline Model-based Adaptable Policy Learning". After being accepted in NeurIPS'21, we conducted experiments inNeoRL. The results can be found in the following table. * In this process, we introduced parts of implementation tricks inthe NeoRL version of MOPOinto MA...
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,...
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...
【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...
model_typeThe SLM being converted.{\"PHI_2\", \"FALCON_RW_1B\", \"STABLELM_4E1T_3B\", \"GEMMA_2B\"} backendThe processor (delegate) used to run the model.{\"cpu\", \"gpu\"} output_dirThe path to the output directory th...
Distributed ledgers are used for trustworthy communication on the blockchain network, which can help consolidate services on the IoT-based UAV network. A hybrid neural model is suggested to keep the blockchain network’s reliability criteria. The drone caching system uses the blocks to set up the...
Daganzo and Ouyang (2019) derived a general model for demand-responsive service to determine the minimum fleet size. A zonal-based service was proposed by Lee et al., (2021b) as a new dispatching system for on-demand transit. Spatially distributed zones enable interzonal planning for flexible...