Recent advances in batch (offline) reinforcement learning have shown promising results in learning from available offline data and proved offline reinforcement learning to be an essential toolkit in learning control policies in a model-free setting. An offline reinforcement learning algorithm applied to a dataset collected by a suboptimal non-learning-based algorithm can result in a policy that outperforms the behavior agent used to collect the data. Such a scenario is frequent in robotics, where existing automation is collecting operational data. Although offline learning techniques can learn from data generated by a sub-optimal behavior agent, there is still an opportunity to improve the sample complexity of existing offline reinforcement learning algorithms by strategically introducing human demonstration data into the training process. To this end, we propose a novel approach that uses uncertainty estimation to trigger the injection of human demonstration data and guide policy training towards optimal behavior while reducing overall sample complexity. Our experiments show that this approach is more sample efficient when compared to a naive way of combining expert data with data collected from a sub-optimal agent. We augmented an existing offline reinforcement learning algorithm Conservative Q-Learning with our approach and performed experiments on data collected from MuJoCo and OffWorld Gym learning environments.
Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；Technical University of Munich（慕尼黑技术大学）；Foundation Robotics Labs, Munich（慕尼黑基础机器人实验室）；Eötvös Loránd University（劳尔·艾尔多迪大学）
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
University of Oslo（奥斯陆大学）；Oslo Metropolitan University（奥斯陆都市大学）；Norwegian University of Science and Technology（挪威科技大学）；Oslo University Hospital（奥斯陆大学医院）；Centre for Artificial Intelligence Research, University of Agder（阿格德尔大学人工智能研究中心）
Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
Department of Electrical Engineering and Automation, Aalto University（阿尔托大学电气工程与自动化系）；School of Computing and Data Science, Xiamen University Malaysia（厦门大学马来西亚分校计算与数据科学学院）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；School of Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程学院）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；MERL（三菱电机研究实验室）
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Polixir Technologies（Polixir科技公司）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
Offline Imitation Learning with Model-based Reverse Augmentation
National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；Nanjing University（南京大学）；School of Intelligence Science and Technology（智能科学与技术学院）；School of Artificial Intelligence（人工智能学院）
JD Technology（京东科技）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学智能产业研究院（AIR））
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without additional online environment interactions. Instead, the agent is provided with a supplementary offline dataset from suboptimal behaviors. Prior works that address this problem either require that expert data occupies the majority proportion of the offline dataset, or need to learn a reward function and perform offline reinforcement learning (RL) afterwards. In this paper, we aim to address the problem without additional steps of reward learning and offline RL training…
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
National University of Defense Technology（国防科技大学）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；Chinese Academy of Sciences（中国科学院）；Delft University of Technology（代尔夫特理工大学）
VLGOR: Visual-Language Knowledge Guided Offline Reinforcement Learning for Generalizable Agents
National Key Laboratory for Novel Software Technology, Nanjing University, China \& School of Artificial Intelligence, Nanjing University, China；Computational Intelligence Center (CIC), School of Computer；Artificial Intelligence, Shandong Jianzhu University, Jinan, 250101, China
Minimal Ingredients for Reward Assignment from Expert Demonstrations
New York University（纽约大学）；New York University Abu Dhabi（纽约大学阿布扎赫尔分校）；Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai（上海前沿科学中心（人工智能与深度学习））
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE（知识驱动的人机智能工程研究中心，教育部）；International Center of Future Science, Jilin University（未来科学国际中心，吉林大学）；JPMorgan AI Research（摩根大通AI研究）；Northwestern Polytechnical University（西北工业大学）；Lehigh University（莱斯大学）
Offline Reinforcement Learning (RL) is a promising approach for learning optimal policies in environments where direct exploration is expensive or unfeasible. However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions. To overcome these issues, we propose an Expert-Supervised RL (ESRL) framework which uses uncertainty quantification for offline policy learning. In particular, we have three contributions: 1) the method can learn safe and opt…
Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset. However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithm…
ServiceNow Research（ServiceNow研究院）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；Google Brain（谷歌大脑）；Polytechnique Montréal（蒙特利尔理工学院）
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. It is as simple as supervised learning and Behavior Cloning (BC), but takes advantage of return information. On datasets collected by policies of similar expertise, implicit BC has been shown to match or outperform explicit BC. Despite the benefits of using implicit models to learn robotic skills via BC, offline RL via Supervised Learning algorithms have been limited to explicit models. We show how implicit models …
ANEO；Centre Borelli - ENS Paris-saclay（巴黎萨克雷高等师范学院博雷利中心）；Criteo AI Lab（科韬人工智能实验室）；Crest, ENSAE（法国国立统计与经济管理学院 Crest 研究中心）
The objective of offline RL is to learn optimal policies when a fixed exploratory demonstrations data-set is available and sampling additional observations is impossible (typically if this operation is either costly or rises ethical questions). In order to solve this problem, off the shelf approaches require a properly defined cost function (or its evaluation on the provided data-set), which are seldom available in practice. To circumvent this issue, a reasonable alternative is to query an expert for few optimal demonstrations in addition to the exploratory data-set. The objective is then to l…
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Northwestern University（西北大学）；George Washington University（乔治华盛顿大学）；Yale University（耶鲁大学）；University of Miami（迈阿密大学）
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private information. We model this interaction as a two-player turn-based game, where one player (Alice, a human) guides the other player (Bob, a machine) towards a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline dataset collected a priori. The offline setting presents two…
University of Pennsylvania（宾夕法尼亚大学）；University of Melbourne（墨尔本大学）
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile regression-based offline imitation learning (IL) algorithm derived via state-occupancy matching. We show that the SMODICE objective admits a simple optimization procedure through an application of Fenchel duality and an analytic solution in tabular MDPs. Without requiring access to expert actions, SMODICE can be effectively applied to three offline IL settings: (i) imitation from observations (IfO), (ii) IfO with dynamics or morphologically mismatched expert, and (iii) example-based reinforcem…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士人工智能实验室 IDSIA/USI/SUPSI）；NNAISENSE
Offline RL methods have been shown to reduce the need for environment interaction by training agents using offline collected episodes. However, these methods typically require action information to be logged during data collection, which can be difficult or even impossible in some practical cases. In this paper, we investigate the potential of using action-free offline datasets to improve online reinforcement learning, name this problem Reinforcement Learning with Action-Free Offline Pretraining (AFP-RL). We introduce Action-Free Guide (AF-Guide), a method that guides online training by extrac…
