强化学习三篇论文 避免遗忘等

2019 年 5 月 24 日 CreateAMind
强化学习三篇论文 避免遗忘等

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

(Submitted on 29 Oct 2018 (v1), last revised 3 May 2019 (this version, v3))

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.

Ray Interference: a Source of Plateaus in Deep Reinforcement Learning

Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu

(Submitted on 25 Apr 2019)

Rather than proposing a new method, this paper investigates an issue present in existing learning algorithms. We study the learning dynamics of reinforcement learning (RL), specifically a characteristic coupling between learning and data generation that arises because RL agents control their future data distribution. In the presence of function approximation, this coupling can lead to a problematic type of 'ray interference', characterized by learning dynamics that sequentially traverse a number of performance plateaus, effectively constraining the agent to learn one thing at a time even when learning in parallel is better. We establish the conditions under which ray interference occurs, show its relation to saddle points and obtain the exact learning dynamics in a restricted setting. We characterize a number of its properties and discuss possible remedies.

Meta-learners' learning dynamics are unlike learners'

Neil C. Rabinowitz

(Submitted on 3 May 2019)

Meta-learning is a tool that allows us to build sample-efficient learning systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just faster learners than their sample-inefficient deep learning (DL) and reinforcement learning (RL) brethren, but that they actually pursue fundamentally different learning trajectories. We study their learning dynamics on three sets of structured tasks for which the corresponding learning dynamics of DL and RL systems have been previously described: linear regression (Saxe et al., 2013), nonlinear regression (Rahaman et al., 2018; Xu et al., 2018), and contextual bandits (Schaul et al., 2019). In each case, while sample-inefficient DL and RL Learners uncover the task structure in a staggered manner, meta-trained LSTM Meta-Learners uncover almost all task structure concurrently, congruent with the patterns expected from Bayes-optimal inference algorithms. This has implications for research areas wherever the learning behaviour itself is of interest, such as safety, curriculum design, and human-in-the-loop machine learning.



强化学习(RL)是机器学习的一个领域,与软件代理应如何在环境中采取行动以最大化累积奖励的概念有关。除了监督学习和非监督学习外,强化学习是三种基本的机器学习范式之一。 强化学习与监督学习的不同之处在于,不需要呈现带标签的输入/输出对,也不需要显式纠正次优动作。相反,重点是在探索(未知领域)和利用(当前知识)之间找到平衡。 该环境通常以马尔可夫决策过程(MDP)的形式陈述,因为针对这种情况的许多强化学习算法都使用动态编程技术。经典动态规划方法和强化学习算法之间的主要区别在于,后者不假设MDP的确切数学模型,并且针对无法采用精确方法的大型MDP。





Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. When it comes to real-world scenarios such as recommendation system and online advertising, however, it is essential to consider the resource consumption of exploration. In practice, there is typically non-zero cost associated with executing a recommendation (arm) in the environment, and hence, the policy should be learned with a fixed exploration cost constraint. It is challenging to learn a global optimal policy directly, since it is a NP-hard problem and significantly complicates the exploration and exploitation trade-off of bandit algorithms. Existing approaches focus on solving the problems by adopting the greedy policy which estimates the expected rewards and costs and uses a greedy selection based on each arm's expected reward/cost ratio using historical observation until the exploration resource is exhausted. However, existing methods are hard to extend to infinite time horizon, since the learning process will be terminated when there is no more resource. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint. HATCH adopts an adaptive method to allocate the exploration resource based on the remaining resource/time and the estimation of reward distribution among different user contexts. In addition, we utilize full of contextual feature information to find the best personalized recommendation. Finally, in order to prove the theoretical guarantee, we present a regret bound analysis and prove that HATCH achieves a regret bound as low as $O(\sqrt{T})$. The experimental results demonstrate the effectiveness and efficiency of the proposed method on both synthetic data sets and the real-world applications.


Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, their performance on previous tasks drops drastically. In this work, we introduce a novel framework to tackle this problem with conditional computation. We equip each convolutional layer with task-specific gating modules, selecting which filters to apply on the given input. This way, we achieve two appealing properties. Firstly, the execution patterns of the gates allow to identify and protect important filters, ensuring no loss in the performance of the model for previously learned tasks. Secondly, by using a sparsity objective, we can promote the selection of a limited set of kernels, allowing to retain sufficient model capacity to digest new tasks.Existing solutions require, at test time, awareness of the task to which each example belongs to. This knowledge, however, may not be available in many practical scenarios. Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available. We validate our proposal on four continual learning datasets. Results show that our model consistently outperforms existing methods both in the presence and the absence of a task oracle. Notably, on Split SVHN and Imagenet-50 datasets, our model yields up to 23.98% and 17.42% improvement in accuracy w.r.t. competing methods.


Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.


Deep reinforcement learning suggests the promise of fully automated learning of robotic control policies that directly map sensory inputs to low-level actions. However, applying deep reinforcement learning methods on real-world robots is exceptionally difficult, due both to the sample complexity and, just as importantly, the sensitivity of such methods to hyperparameters. While hyperparameter tuning can be performed in parallel in simulated domains, it is usually impractical to tune hyperparameters directly on real-world robotic platforms, especially legged platforms like quadrupedal robots that can be damaged through extensive trial-and-error learning. In this paper, we develop a stable variant of the soft actor-critic deep reinforcement learning algorithm that requires minimal hyperparameter tuning, while also requiring only a modest number of trials to learn multilayer neural network policies. This algorithm is based on the framework of maximum entropy reinforcement learning, and automatically trades off exploration against exploitation by dynamically and automatically tuning a temperature parameter that determines the stochasticity of the policy. We show that this method achieves state-of-the-art performance on four standard benchmark environments. We then demonstrate that it can be used to learn quadrupedal locomotion gaits on a real-world Minitaur robot, learning to walk from scratch directly in the real world in two hours of training.

ICLR 2020 高质量强化学习论文汇总
11+阅读 · 2019年11月11日
16+阅读 · 2019年7月6日
6+阅读 · 2019年1月18日
强化学习的Unsupervised Meta-Learning
7+阅读 · 2019年1月7日
Unsupervised Learning via Meta-Learning
32+阅读 · 2019年1月3日
RL 真经
4+阅读 · 2018年12月28日
Hierarchical Imitation - Reinforcement Learning
16+阅读 · 2018年5月25日
11+阅读 · 2017年8月2日
强化学习 cartpole_a3c
9+阅读 · 2017年7月21日
91+阅读 · 2020年2月8日
111+阅读 · 2020年2月1日
【强化学习资源集合】Awesome Reinforcement Learning
57+阅读 · 2019年12月23日
75+阅读 · 2019年10月11日
47+阅读 · 2019年10月10日
TensorFlow 2.0 学习资源汇总
46+阅读 · 2019年10月9日
158+阅读 · 2019年10月9日
Mengyue Yang,Qingyang Li,Zhiwei Qin,Jieping Ye
5+阅读 · 2020年4月2日
Davide Abati,Jakub Tomczak,Tijmen Blankevoort,Simone Calderara,Rita Cucchiara,Babak Ehteshami Bejnordi
5+阅读 · 2020年3月31日
Continual Unsupervised Representation Learning
Dushyant Rao,Francesco Visin,Andrei A. Rusu,Yee Whye Teh,Razvan Pascanu,Raia Hadsell
5+阅读 · 2019年10月31日
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu,Deirdre Quillen,Zhanpeng He,Ryan Julian,Karol Hausman,Chelsea Finn,Sergey Levine
29+阅读 · 2019年10月24日
Tuomas Haarnoja,Aurick Zhou,Sehoon Ha,Jie Tan,George Tucker,Sergey Levine
6+阅读 · 2018年12月26日
Hierarchical Deep Multiagent Reinforcement Learning
Hongyao Tang,Jianye Hao,Tangjie Lv,Yingfeng Chen,Zongzhang Zhang,Hangtian Jia,Chunxu Ren,Yan Zheng,Changjie Fan,Li Wang
6+阅读 · 2018年9月25日
A Multi-Objective Deep Reinforcement Learning Framework
Thanh Thi Nguyen
9+阅读 · 2018年6月27日
Abhishek Kumar,Daisuke Kawahara,Sadao Kurohashi
3+阅读 · 2018年6月16日
Yaodong Yang,Rui Luo,Minne Li,Ming Zhou,Weinan Zhang,Jun Wang
3+阅读 · 2018年6月12日
Shikun Liu,Edward Johns,Andrew J. Davison
16+阅读 · 2018年3月28日