"Assessing generalization in deep reinforcement learning." arXiv preprint arXiv:1810.12282 (2018). 前言 (注意:这是一篇实验报告式的文章) 现在的RL算法很容易overfit到固定的环境上,因为他们通常都是在测试集上训练的。RL算法对环境的变化很敏感。 泛化性(generalization)是AI系统非常重要的一个性能。包含两个...
Packer, Charles, et al. "Assessing generalization in deep reinforcement learning."arXiv preprint arXiv:1810.12282(2018). 实验场景是CartPole, MountainCar, Acrobat, Pendulum, HalfCheetah, Hopper 再加上参数扰动。参数扰动分为 D、R、E(分别对应表 2 的三列),即使用不同的可扰动参数。 FF 表示feedforwar...
Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments. To alleviate this problem, recent work has proposed the use of data augmentation. However, different tasks tend to benefit from different types of augmentations and selecting the right one typically ...
Deep reinforcement learning (RL) agents often fail to generalize to unseen scenarios, even when they are trained on many instances of semantically similar environments. Data augmentation has recently been shown to improve the sample efficiency and generalization of RL agents. However, different tasks ...
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning / ICLR 2020 - pokaxpoka/netrand
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning https://arxiv.org/abs/1806.07937 4. 死记硬背型过拟合 A Study on Overfitting in Deep Reinforcement Learning https://arxiv.org/abs/1804.06893 Quantifying Generalization in Reinforcement Learning ...
Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. 深度强化学习算法已经展示了令人信服的能力可以学习高维环境中的复杂控制策略 However, despite the ever-increasing performance on popular benchmarks like the Arcade ...
这里权重λ通过从贝塔分布采样得到 对应的所有的监督信号都要做相应的插值 比如PPO里面advantage和action DQN里面也同理。然后整个方法就介绍完了,就是这么简单。。 总结:感觉也太简单粗暴了,这也可以是NIPS,还是有点吃惊的,可能就是效果不错?不过这也给了我们这些人一些中NIPS的希望吧。
This repository contains the code for the following paper presented at the Deep RL Workshop, NeurIPS 2021: Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning. Citation If you use this code, please cite our paper: Nafi, N.M., Glasscock, C. and...
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of gener...