The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially dangerous. Existing off-policy algorithms have limited performance on static datasets due to extrapolation errors from out-of-distribution actions. This leads to the challenge of constraining the policy to select actions within the support of the dataset during training. We propose to simply learn the Policy in the Latent Action Space (PLAS) such that this requirement is naturally satisfied. We evaluate our method on continuous control benchmarks in simulation and a deformable object manipulation task with a physical robot. We demonstrate that our method provides competitive performance consistently across various continuous control tasks and different types of datasets, outperforming existing offline reinforcement learning methods with explicit constraints. Videos and code are available at https://sites.google.com/view/latent-policy.
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）
Offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. Offline RL enables extensive use and re-use of historical datasets, while also alleviating safety concerns associated with online exploration, thereby expanding the real-world applicability of RL. Most prior work in offline RL has focused on tasks with compact state representations. However, the ability to learn directly from rich observation spaces like images is critical for real-world applications such as robotics. In this work, we build on recent advances in mod…
University of Freiburg（弗赖堡大学）；University of Technology Nuremberg（纽伦堡工业大学）
Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still impose a major challenge in offline robot control. While a number of prior methods aimed to address this setting with variants of imitation and offline reinforcement learning, the learned behavior is typically narrow and often struggles to reach configurable long-horizon goals. As both paradigms have complementary strengths and weaknesses, we propose a novel hierarchical approach that combines the strengths of both methods to learn task-agnostic long-horizon policies from high-dimensional camera observ…
Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fast personalization to non-stationary users. The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models. We call this problem a non-stationary latent bandit. We propose Thompson sampling algorithms for regret minimization in non-stationary latent bandits, analyze th…
Offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions. There are two major challenges in this setting: (1) extrapolation error caused by approximating the value of state-action pairs not well-covered by the training data and (2) distributional shift between behavior and inference policies. One way to tackle these problems is to induce conservatism - i.e., keeping the learned policies closer to the behavioral ones. To achieve this, we build upon recent works on learning policies in latent action spaces and use a…
Tsinghua University（清华大学）；Stanford University（斯坦福大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society, CUHK Shenzhen（香港中文大学（深圳）深圳市人工智能与机器人研究院）
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting is particularly well-suited for continuous control robotic applications for which online data collection based on trial-and-error is costly and potentially unsafe. In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios, such as data from several human demonstrators or from policies that act with different purposes. Unfortunately, such datasets can exacerbate the distribution…
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to identify the latent state, after which it can act optimally. This setting is a natural midpoint between online and offline learning---complex models can be learned offline with the agent identifying latent state online---of practical relevance in, say, recommender systems. In this work, we propose general algorithms for this setting, based on both upper confidence bounds (UCBs) and Thompson sampling. Our methods are c…
Manifold-Constrained Energy-Based Transition Models for Offline Reinforcement Learning
Department of Electrical and Computer Engineering, George Washington University（乔治华盛顿大学电气与计算机工程系）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea（首尔国立大学电气与计算机工程系）；Department of Automotive Engineering, Ajou University, Gyeonggi-do, South Korea（全州大学汽车工程系）
Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning
Department of Electrical and Computer Engineering and ASRI, Seoul National University（电气与计算机工程系和首尔国立大学ASRI）；Independent researcher（独立研究者）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived "solved" benchmark settings such as "MountainCar" and "CartPole". To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by…
Offline Reinforcement Learning with Discrete Diffusion Skills
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Beijing Jiaotong University（北京交通大学）；Tsinghua University（清华大学）；Beijing Key Laboratory of Traffic Data Analysis and Mining（北京交通数据分析与挖掘重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Technion Institute of Technology（以色列理工学院）；Nvidia Research（英伟达研究院）
Model-based offline reinforcement learning approaches generally rely on bounds of model error. Estimating these bounds is usually achieved through uncertainty estimation methods. In this work, we combine parametric and nonparametric methods for uncertainty estimation through a novel latent space based metric. In particular, we build upon recent advances in Riemannian geometry of generative models to construct a pullback metric of an encoder-decoder based forward model. Our proposed metric measures both the quality of out-of-distribution samples as well as the discrepancy of examples in the dat…
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Sun Yat-sen University（中山大学）；Lehigh University（里海大学）；College of Computing and Data Science, NTU（南洋理工大学计算与数据科学学院）
Meta AI (FAIR)（Meta人工智能（基础人工智能研究团队））；ETH Zürich（苏黎世联邦理工学院）
Demonstrations provide insight into relevant state or action space regions, bearing great potential to boost the efficiency and practicality of reinforcement learning agents. In this work, we propose to leverage demonstration datasets by combining skill learning and sequence modeling. Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy. The sequence model forms a latent space prior over plausible demonstration behaviors to accelerate learning of high-level policies. We show how to acquire such priors…
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
University of Maryland（马里兰大学）；Microsoft Research（微软研究院）；University of Southern California（南加州大学）；Tsinghua University（清华大学）；Shanghai Qi Zhi Institute（上海期智研究院）
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Institute of General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Department of Statistics and Data Science, UCLA（UCLA统计与数据科学系）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Department of Computer Science, UCLA（UCLA计算机科学系）
Latent Action World Models for Control with Unlabeled Trajectories
Technical University of Munich（慕尼黑技术大学）；Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；Eötvös Loránd University Budapest（布达佩斯欧多立大学）；Foundation Robotics Labs（基础机器人实验室）
Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Princeton University（普林斯顿大学）；Princeton Plasma Physics Lab（普林斯顿等离子物理实验室）；The University of Hong Kong（香港大学）；Lila Sciences
Duke University（杜克大学）；North Carolina State University（北卡罗来纳州立大学）
Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent …
Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning. In this work, we instead focus on learning recommendation policies in the pure batch or offline setting, i.e. learning policies solely from offline historical interaction logs or batch data generated from an unknown and sub-optimal behavior policy, without further access to data from the real-world or user-behavior models. We propose BCD4Rec: Batch-…
