Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a learned policy. However, Bayesian IRL is computationally intractable for high-dimensional problems because each sample from the posterior requires solving an entire Markov Decision Process (MDP). While there exist non-Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis. We propose Bayesian Reward Extrapolation (B-REX), a highly efficient, preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks. Our approach uses successor feature representations and preferences over demonstrations to efficiently generate samples from the posterior distribution over the demonstrator's reward function without requiring an MDP solver. Using samples from the posterior, we demonstrate how to calculate high-confidence bounds on policy performance in the imitation learning setting, in which the ground-truth reward function is unknown. We evaluate our proposed approach on the task of learning to play Atari games via imitation learning from pixel inputs, with no access to the game score. We demonstrate that B-REX learns imitation policies that are competitive with a state-of-the-art deep imitation learning method that only learns a point estimate of the reward function. Furthermore, we demonstrate that samples from the posterior generated via B-REX can be used to compute high-confidence performance bounds for a variety of evaluation policies. We show that high-confidence performance bounds are useful for accurately ranking different evaluation policies when the reward function is unknown. We also demonstrate that high-confidence performance bounds may be useful for detecting reward hacking.
University of Cambridge（剑桥大学）；University of California, Los Angeles（加州大学洛杉矶分校）；The Alan Turing Institute（艾伦·图灵研究所）
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the property that alternatives yielding higher rewards are preferred over alternatives yielding lower rewards. However, in many settings, preferences are based on multiple, often competing, objectives; a single reward function is not adequate to represent such preferences. This paper proposes a method for inferring multi-objective reward-based representations of an agent's observed preferences. We model the agent's prio…
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. However, poor feedback-efficiency still remains a problem in current preference-based RL algorithms, as tailored human feedback is very expensive. To handle this issue, previous methods have mainly focused on improving query selection and policy initialization. At the same time, rece…
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games…
University of Bristol（布里斯托大学）；CortAIx, Thales（泰雷兹公司 CortAIx）
The potential of reinforcement learning (RL) to deliver aligned and performant agents is partially bottlenecked by the reward engineering problem. One alternative to heuristic trial-and-error is preference-based RL (PbRL), where a reward function is inferred from sparse human feedback. However, prior PbRL methods lack interpretability of the learned reward structure, which hampers the ability to assess robustness and alignment. We propose an online, active preference learning algorithm that constructs reward functions with the intrinsically interpretable, compositional structure of a tree. Usi…
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from human feedback. Our method improves prior work by generalising human preferences, reducing the burden on the user and increasing the robustness of the reward model. We demonstrate this with a number of experiments.
Preference-based Reinforcement Learning (PbRL) methods utilize binary feedback from the human in the loop (HiL) over queried trajectory pairs to learn a reward model in an attempt to approximate the human's underlying reward function capturing their preferences. In this work, we investigate the issue of a high degree of variability in the initialized reward models which are sensitive to random seeds of the experiment. This further compounds the issue of degenerate reward functions PbRL methods already suffer from. We propose a data-driven reward initialization method that does not add any addi…
Specifying rewards for reinforcement learned (RL) agents is challenging. Preference-based RL (PbRL) mitigates these challenges by inferring a reward from feedback over sets of trajectories. However, the effectiveness of PbRL is limited by the amount of feedback needed to reliably recover the structure of the target reward. We present the PRIor Over Rewards (PRIOR) framework, which incorporates priors about the structure of the reward function and the preference feedback into the reward learning process. Imposing these priors as soft constraints on the reward learning objective reduces the amou…
KAIST（韩国科学技术院）；University of Michigan（密歇根大学）；LG AI Research（LG人工智能研究院）；UC Berkeley（加州大学伯克利分校）
Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor's preference between the two agent behaviors. However, preference-based learning often requires a large amount of human feedback, making it difficult to apply this approach to various applications. This data-efficiency problem, on the other hand, has been typically addressed by using unlabeled samples or data augmentation techniques in the context of supervised learning. Motivated by the recent su…
Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain impractical due to the burdensome number of labels required from the human, even for relatively simple tasks. In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks. We hypothesize that REED-based methods better partition the state-action space and facilitate generali…
PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
TCS Research, \ of CSE, IIT Madras India；Department of Computing Science, \ of Alberta Canada；Qatar Computing Research Institute, \ Bin Khalifa University Qatar；Department of Data Science \& AI, Wadhwani School of Data Science \& AI, IIT Madras India；TCS Research, \ of CSE, IIT Madras；Department of Computing Science, \ of Alberta；Qatar Computing Research Institute, \ Bin Khalifa University；Department of Data Science \& AI, Wadhwani School of Data Science \& AI, IIT Madras
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Applied Research Laboratories, The University of Texas at Austin（德克萨斯大学奥斯汀分校应用研究实验室）
Bayesian reward learning from demonstrations enables rigorous safety and uncertainty analysis when performing imitation learning. However, Bayesian reward learning methods are typically computationally intractable for complex control problems. We propose Bayesian Reward Extrapolation (Bayesian REX), a highly efficient Bayesian reward learning algorithm that scales to high-dimensional imitation learning problems by pre-training a low-dimensional feature encoding via self-supervised tasks and then leveraging preferences over demonstrations to perform fast Bayesian inference. Bayesian REX can lea…
Learning Optimal Advantage from Preferences and Mistaking it for Reward
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Research（谷歌研究院）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；UC Berkeley（加州大学伯克利分校）；Sony AI（索尼人工智能）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization
University of Manchester（曼彻斯特大学）；University of Edinburgh（爱丁堡大学）；Aalto University（阿尔托大学）；University of Amsterdam（阿姆斯特丹大学）；ELLIS Institute Finland（芬兰ELLIS研究所）
Models of human preference for learning reward functions
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Bosch（博世）；Google Research（谷歌研究院）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Sony AI（索尼人工智能研究院）
Reinforcement Learning has suffered from poor reward specification, and issues for reward hacking even in simple enough domains. Preference Based Reinforcement Learning attempts to solve the issue by utilizing binary feedbacks on queried trajectory pairs by a human in the loop indicating their preferences about the agent's behavior to learn a reward model. In this work, we present a state augmentation technique that allows the agent's reward model to be robust and follow an invariance consistency that significantly improved performance, i.e. the reward recovery and subsequent return computed u…
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are encoded by a utility function that is not known in closed form but can be estimated by asking the DM to express preferences over pairs of outcome vectors. To address this problem, we develop Bayesian optimization with preference exploration, a novel framework that alternates between interactive real-time preference learning with the DM via pairwise comparisons between outcomes, and Bayesian optimization with a learne…
A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems. We propose symbolic reward machines for incorporating high-level task knowledge when specifying the reward signals. Symbolic reward machines augment existing reward machine formalism by allowing transitions to carry predicates and symbolic reward outputs. This formalism lends itself well to inverse reinforcement learning, whereby the key challenge is determining appropriate assignments to the symbolic values from a few expert demonstrations. We propose a hierarchical Baye…
Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
Department of Electrical and Computer Engineering, Duke University, Durham, US（电子工程系，杜克大学，达勒姆，美国）；Department of Computer Science, Duke University, Durham, US（计算机科学系，杜克大学，达勒姆，美国）；Department of Biostatistics and Bioinformatics, Duke University, Durham, US（生物统计学与生物信息学系，杜克大学，达勒姆，美国）；Department of Statistics and Data Science, Yale University, New Haven, US（统计学与数据科学系，耶鲁大学，纽 Haven，美国）
GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
College of Computing and Data Science,Nanyang Technological University (NTU), Singapore（南洋理工大学计算与数据科学学院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly(LILY),NTU,Singapore（南洋理工大学与UBC联合卓越老年活跃生活研究中心（LILY））
IRPM: Intergroup Relative Preference Modeling for Pointwise Generative Reward Models
HUJING Digital Media \& Entertainment Group (XingYun Lab), Beijing, China；Department of Automation, Tsinghua University, Beijing, China；Fudan University, Shanghai, China；Beihang University, Beijing, China；Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学计算机科学与工程保罗·G·艾伦学校）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（德国图宾根马克斯·普朗克智能系统研究所）；Jinesis Lab, University of Toronto & Vector Institute（多伦多大学Jinesis实验室及向量研究所）
