Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed. First, in real world settings, when an agent attempts a task and fails, the environment must somehow "reset" so that the agent can attempt the task again. While easy in simulation, this could require considerable human effort in the real world, especially if the number of trials is very large. Second, real world learning often involves complex, temporally extended behavior that is often difficult to acquire with random exploration. While these two problems may at first appear unrelated, in this work, we show how a single method can allow an agent to acquire skills with minimal supervision while removing the need for resets. We do this by exploiting the insight that the need to "reset" an agent to a broad set of initial states for a learning task provides a natural setting to learn a diverse set of "reset-skills". We propose a general-sum game formulation that balances the objectives of resetting and learning skills, and demonstrate that this approach improves performance on reset-free tasks, and additionally show that the skills we obtain can be used to significantly accelerate downstream learning.
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
Department of Electrical and Computer Engineering (ECE), Seoul National University（电子与计算机工程系，首尔国立大学）；Interdisciplinary Program in Artificial Intelligence (IPAI), Seoul National University（人工智能交叉计划，首尔国立大学）；Samsung Advanced Institute of Technology（三星先进技术研究所）
The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill dis…
University of Southern California（南加州大学）；Amazon（亚马逊）
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. A great promise of continual learning is to endow robots with this ability, by using their accumulated knowledge and experience from prior skills. We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies. We show that storing skill policies, careful pr…
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）
Continual Learning as Computationally Constrained Reinforcement Learning
Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）；Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Department of Management Science and Engineering, Stanford University（管理科学与工程系，斯坦福大学）
Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）
The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent'…
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
Facebook AI Research（Facebook 人工智能研究院）；UC Berkeley EECS（加州大学伯克利分校电气工程与计算机科学系）
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay app…
Machine Learning Research Group（机器学习研究组）；University of Oxford（牛津大学）；Microsoft Research Cambridge（微软剑桥研究院）
Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capacity and time while not suffering from catastrophic forgetting. In this work we study the efficiency of continual learning systems, taking inspiration from human learning. In particular, inspired by the mechanisms of sleep, we evaluate popular pruning-based continual learning algorithms, using PackNet as a case study. First, we identify that weight freezing, which is used in continual learning without biological justi…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Chinese Academy of Sciences（中国科学院）；Tianjin University（天津大学）
The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones. To address such the 'sensitivity-stability' dilemma, most previous efforts have been contributed to minimizing the empirical risk with different parameter regularization terms and episodic memory, but rarely exploring the usages of the weight loss landscape. In this paper, we investigate the relationship between the weight loss landscape and sensitivity-stability in the continual learning scenario, based on which, we propose a novel me…
Apprente, Inc.（Apprente公司）；University of Hamburg（汉堡大学）；PaigeAI（PaigeAI公司）；Rochester Institute of Technology（罗切斯特理工学院）；Cornell Tech（康奈尔科技学院）
Continual learning refers to the ability of a biological or artificial system to seamlessly learn from continuous streams of information while preventing catastrophic forgetting, i.e., a condition in which new incoming information strongly interferes with previously learned representations. Since it is unrealistic to provide artificial agents with all the necessary prior knowledge to effectively operate in real-world conditions, they must exhibit a rich set of learning capabilities enabling them to interact in complex environments with the aim to process and make sense of continuous streams of…
Knowledge Fusion of Large Language Models Via Modular SkillPacks
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Huazhong University of Science and Technology（华中科技大学）
Monash University（莫纳什大学）；South China University of Technology（华南理工大学）
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a reinforcement model to learn a policy for each task sequentially due to catastrophic forgetting. Specifically, the model would forget knowledge it learned in the past when trained on a new task. We consider this challenge from two perspectives: i) acquiring task-specific skills is difficult since task information and rewards are not highly related; ii) learning knowledge from previous experience is difficult in continuou…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Peking University（北京大学）；Google Research（谷歌研究院）
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge…
Meta AI Research（Meta人工智能研究院）；CNRS-ISIR, Sorbonne University（法国国家科学研究中心智能系统与机器人研究所，索邦大学）；McGill University（麦吉尔大学）；Mila（米拉人工智能研究所）；Bosch Research（博世研究院）；Ubisoft France（育碧法国）
The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that scale poorly with the number of tasks. In this work, we aim to strike a better balance between an agent's size and performance by designing a method that grows adaptively depending on the task sequence. We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learn…
Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；Dynatrace Research（Dynatrace研究院）；Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所（IARAI））
In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to detect and cope with due to their continuous nature. Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them. We propose Reactive Exploration to track and react …
ENSTA ParisTech（巴黎高等先进技术学院）；INRIA Flowers（法国国家信息与自动化研究所Flowers实验室）；University of Bologna（博洛尼亚大学）
Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and for…
While reinforcement learning (RL) provides a framework for learning through trial and error, translating RL algorithms into the real world has remained challenging. A major hurdle to real-world application arises from the development of algorithms in an episodic setting where the environment is reset after every trial, in contrast with the continual and non-episodic nature of the real-world encountered by embodied agents such as humans and robots. Prior works have considered an alternating approach where a forward policy learns to solve the task and the backward policy learns to reset the envi…
We study learning control in an online reset-free lifelong learning scenario, where mistakes can compound catastrophically into the future and the underlying dynamics of the environment may change. Traditional model-free policy learning methods have achieved successes in difficult tasks due to their broad flexibility, but struggle in this setting, as they can activate failure modes early in their lifetimes which are difficult to recover from and face performance degradation as dynamics change. On the other hand, model-based planning methods learn and adapt quickly, but require prohibitive leve…
Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Episodic memory plays an important role in the behavior of animals and humans. It allows the accumulation of information about current state of the environment in a task-agnostic way. This episodic representation can be later accessed by down-stream tasks in order to make their execution more efficient. In this work, we introduce the neural architecture with shared episodic memory (SEM) for learning and the sequential execution of multiple tasks. We explicitly split the encoding of episodic memory and task-specific memory into separate recurrent sub-networks. An agent augmented with SEM was ab…
ENSTA Paris（巴黎高等先进技术学院）；Institut Polytechnique de Paris（巴黎理工学院）；INRIA（法国国家信息与自动化研究所）；Thales（泰雷兹集团）；University of Bologna（博洛尼亚大学）
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and objective criteria are never available at once. The evolution of the learning process is modeled by a sequence of learning experiences where the goal is to be able to learn new skills all along the sequence without forgetting what has been previously learned. Continual learning also aims at the same time at optimizing the memory, the computation power and the speed during the learning process. An important challenge for ma…
Universität Hamburg（汉堡大学）；Rochester Institute of Technology（罗切斯特理工学院）；Heriot-Watt University（赫瑞-瓦特大学）
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing chall…
University of California, Los Angeles（加州大学洛杉矶分校）；Salesforce Research（Salesforce 研究院）
Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can …
Department of Electrical Engineering KU Leuven, Belgium（卢森堡鲁文大学电气工程系）；Department of Electrical and Computer Engineering University of Texas at San Antonio, USA（德克萨斯大学圣安东尼奥分校电气与计算机工程系）
