The function approximators employed by traditional image-based Deep Reinforcement Learning (DRL) algorithms usually lack a temporal learning component and instead focus on learning the spatial component. We propose a technique, Temporal Shift Reinforcement Learning (TSRL), wherein both temporal, as well as spatial components are jointly learned. Moreover, TSRL does not require additional parameters to perform temporal learning. We show that TSRL outperforms the commonly used frame stacking heuristic on both of the Atari environments we test on while beating the SOTA for one of them. This investigation has implications in the robotics as well as sequential decision-making domains.
Honda Research Institute US（美国本田研究所）；Stanford University（斯坦福大学）；University of California, Berkeley（加州大学伯克利分校）
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate drastically different outcomes remains an open problem with designing autonomous systems that operate in human environments. In this work, we show that explicitly inferring the latent state and encoding spatial-temporal relationships in a reinforcement learning framework can help address this difficulty. We encode prior knowledge on the latent states of other drivers through a framework that combines the reinforcement…
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Research Center Trustworthy Data Science and Security, TU Dortmund University（多特蒙德工业大学可信数据科学与安全研究中心）；School for Engineering of Matter, Transport, and Energy at Arizona State University（亚利桑那州立大学物质、运输与能源工程学院）
The temporal Credit Assignment Problem (CAP) is a well-known and challenging task in AI. While Reinforcement Learning (RL), especially Deep RL, works well when immediate rewards are available, it can fail when only delayed rewards are available or when the reward function is noisy. In this work, we propose delegating the CAP to a Neural Network-based algorithm named InferNet that explicitly learns to infer the immediate rewards from the delayed rewards. The effectiveness of InferNet was evaluated on two online RL tasks: a simple GridWorld and 40 Atari games; and two offline RL tasks: GridWorld…
From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training
School of Computer Science & Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University（北京交通大学交通数据挖掘与具身智能北京市重点实验室）；Beijing Jiaotong University（北京交通大学）
University of Edinburgh（爱丁堡大学）；Aalto University（阿尔托大学）；University of Wisconsin – Madison（威斯康星大学麦迪逊分校）
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. The changed pixels can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image…
A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics
Southern University of Science and Technology（南方科技大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Fudan University（复旦大学）；Shanghai Academy of AI for Science（上海人工智能研究院）
Northeastern University（东北大学）；Khoury College of Computer Sciences（库利计算机科学学院）
Q($σ$) is a recently proposed temporal-difference learning method that interpolates between learning from expected backups and sampled backups. It has been shown that intermediate values for the interpolation parameter $σ\in [0,1]$ perform better in practice, and therefore it is commonly believed that $σ$ functions as a bias-variance trade-off parameter to achieve these improvements. In our work, we disprove this notion, showing that the choice of $σ=0$ minimizes variance without increasing bias. This indicates that $σ$ must have some other effect on learning that is not fully understood. As a…
STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning
Beijing Key Laboratory of Embodied Intelligence Systems, Department of Automation, Tsinghua University, Beijing, China（北京智能体智能系统重点实验室，自动化系，清华大学，北京，中国）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China（复杂系统认知与决策智能重点实验室，自动化研究所，中国科学院，北京，中国）
Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little expert data as possible. In this paper we present an algorithm that imitates the higher-level strategy of the expert rather than just imitating the expert on action level, which we hypothesize requires less expert data and makes training more stable. As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Lear…
McGill University（麦吉尔大学）；Mila（米拉研究所）；University of Waterloo（滑铁卢大学）；DarwinAI（达尔文人工智能公司）；DeepMind（深度思维）
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which the deep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a…
This paper addresses the problem of learning control policies for mobile robots, modeled as unknown Markov Decision Processes (MDPs), that are tasked with temporal logic missions, such as sequencing, coverage, or surveillance. The MDP captures uncertainty in the workspace structure and the outcomes of control decisions. The control objective is to synthesize a control policy that maximizes the probability of accomplishing a high-level task, specified as a Linear Temporal Logic (LTL) formula. To address this problem, we propose a novel accelerated model-based reinforcement learning (RL) algorit…
Ant Group（蚂蚁集团）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in reinforcement learning. To this end, we present a novel framework of the model-based reinforcement learning where the agent's actions and observations are asynchronous stochastic discrete events occurring in continuous-time. We model the d…
Northwestern Polytechnical University（西北工业大学）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Academy of Industrial Internet, Shaanxi Branch of China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司陕西省分公司工业互联网研究院）
Deep reinforcement learning (DRL) has made significant achievements in many real-world applications. But these real-world applications typically can only provide partial observations for making decisions due to occlusions and noisy sensors. However, partial state observability can be used to hide malicious behaviors for backdoors. In this paper, we explore the sequential nature of DRL and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a set of temporal constraints on a sequence of observations rather than a single observation, and effect can be kept in a controllable…
This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e., never-seen-before, generalisation of formally specified instructions. In particular, we present a neuro-symbolic framework where a symbolic module transforms TL specifications into a form that helps the training of a DRL agent targeting generalisation, while a neural module learns systematically to solve the given tasks. We study the emergence of systematic learning in different settings and find that the architecture of the convolutional l…
University of Trento（特伦托大学）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past. However, providing complete observations of numerous steps can be excessive. Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision. Our method (TempAl) aligns temporally-close frames, revealing a general, slowly varying state of the environment. This procedure is based on contras…
We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expert data in a contextual MDP setup. We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup. We then discuss the problem of distribution shift between the expert data and the online environment when the data is only partially observable. We prove possibility and impossibility results for imitation lear…
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Renmin University of China（中国人民大学）；Peking University（北京大学）；King’s College London（伦敦国王学院）；National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能全国重点实验室）
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
McGill University（麦吉尔大学）；Microsoft Research Montreal（微软研究院蒙特利尔分部）；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Sony AI（索尼人工智能公司）；Google DeepMind（谷歌DeepMind）
Stanford University（斯坦福大学）；University of California, Berkeley（加州大学伯克利分校）
Reinforcement learning algorithms can acquire policies for complex tasks autonomously. However, the number of samples required to learn a diverse set of skills can be prohibitively large. While meta-reinforcement learning methods have enabled agents to leverage prior experience to adapt quickly to new tasks, their performance depends crucially on how close the new task is to the previously experienced tasks. Current approaches are either not able to extrapolate well, or can do so at the expense of requiring extremely large amounts of data for on-policy meta-training. In this work, we present m…
