论文分享:Guiding Pretraining in Reinforcement Learning with Large Language Models 这篇文章主要研究的问题领域是无监督强化学习(URL),即如何在缺乏奖励函数的情况下,通过intrinsic reward对环境进行探索。本文提出的方法ELLM(Exploring with LLMs),利用LLM给出建议目标,引导策略预训练,让agent做出更多看起来对人类有意...
Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors. Reinforcement learning is a fundamental process by which organisms learn to achieve goals from their interactions with the environment. Using evolutionary ... Niv,Yael,Joel,... - 《...
Reinforcement learning(RL) problems with uncertainty and hidden state present significant obstacles to prevailing RL methods.In this paper,a novel approximate algorithm,called Memetic algorithm based Q-Learning(MA-Q-Learning),is proposed as a means to solve the POMDP problems which has such uncertainty...
《The State of Sparse Training in Deep Reinforcement Learning》是一篇ICML2022的论文,这篇论文系统性的分析了目前CV领域中sparse training技术应用到DRL的场景中的性能和实验细节,并分析了sparse training结合RL时一些RL设定的影响,同时验证了CV领域中稀疏网络在相同参数量的情况下性能可以比稠密网络更好的结果在RL设...
The optimal strategy must be re-learned when environment changes. The learning algorithm cannot converge to optimal strategy if the interval between the changes is shorter than the duration of strategy converging. In this paper, a hierarchical reinforcement learning approach adapting to dynamic ...
We see that there a lot of commonalities with the reinforcement learning (RL) setup, where the agent observes the environment and acts upon it in order to change its state towards better states (states with higher rewards). To this end we introduce RecoGym, an RL environment for ...
二、Playing Atari with Deep Reinforcement Learning(CoRR 2013) 为了减轻相关数据和非平稳分布的问题,我们使用一种经验回放机制,该机制随机采样先前的转换,从而使训练分布在许多过去的行为上变得平滑。 与TD-Gammon和类似的在线方法相比,我们使用一种称为经验回放的技术,在该技术中,我们将智能体在每个时间步骤的经验存...
These environments provide new playgrounds for RL research in the management of electricity networks that do not require an extensive knowledge of the underlying dynamics of such systems. Along with this work, we are releasing an implementation of an introductory toy-environment, ANM6-Easy, designed...
CHILD, an agent capable of Continual, Hierarchical, Incremental Learning and Development is proposed, described, tested, and evaluated in this dissertation. CHILD accumulates useful behaviors in reinforcement environments by using the Temporal Transition Hierarchies learning algorithm, also derived in the ...
In-context reinforcement learning with algorithm distillation[J]. arXiv preprint arXiv:2210.14215, 2022. arxiv.org/pdf/2210.1421 1.摘要- 关键概念 : 该论文提出了一种用于将强化学习(RL)算法提炼成神经网络的方法是什么? 论文提出的方法是算法蒸馏(Algorithm Distillation,简称AD),这是一种将强化学习(RL)...