Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Sven Collette, Wolfgang M Pauli, Peter Bossaerts, John O'Doherty + 1 more
'David Badre'] In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while…
Dong Han, Beni Mulyana, Vladimir Stankovic, Samuel Cheng + 1 more
'Alberto Borboni'] Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin…
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL)…
Shuvendu K. Lahiri, Chao Wang, Marcell Vazquez-Chanlatte, Sanjit A. Seshia
'Sanjit A. Seshia'] In many settings, such as robotics, demonstrations provide a natural way to specify tasks. However, most methods for learning from demonstrations either do not provide guarantees that the learned artifacts can be safely composed or do not explicitly capture temporal properties. Motivated by this…
Lei Zhao, Mengdi Wang, Yu Bai
Inverse Reinforcement Learning (IRL)—the problem of learning reward functions from demonstrations of an expert policy—plays a critical role in developing intelligent systems. While widely used in applications, theoretical understandings of IRL present unique challenges and remain less developed compared with standard…
Nirjhar Das, Arpan Chattopadhyay
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a set of trajectory demonstrations for the optimal policy. In this work…
Nigini Oliveira, Jasmine Li, Koosha Khalvati, Rodolfo Cortes Barragan + 4 more
Constructing a universal moral code for artificial intelligence (AI) is challenging because human cultures have different values, norms, and social practices. We therefore argue that AI systems should adapt to culture based on observation: Just as a child raised in a particular culture learns the specific values…
Justin Chow, Yunran Yang, Brokoslaw Laschowski
Inverse reinforcement learning can recover reward functions from observed behavior, but interpreting those rewards remains a fundamental challenge for understanding intelligent behavior and decision-making. To address this challenge, we introduce a novel framework for reward interpretation that combines reward-function…
Aditi Jha, Victor Geadah, Jonathan W. Pillow
Understanding complex animal behavior is crucial for linking brain computation to observed actions. While recent research has shifted towards modeling behavior as a dynamic process, few approaches exist for modeling long-term, naturalistic behaviors such as navigation. We introduce discrete Dynamical Inverse…
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
We provide an original theoretical study of Inverse Reinforcement Learning (IRL) through the lens of reward compatibility, a novel framework to quantify the compatibility of a reward with the given expert's demonstrations. Intuitively, a reward is more compatible with the demonstrations the closer the performance of…
Zheng Wu, Fangbing Qu, Lin Yang, Jianwei Gong + 1 more
With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior…
Ruohan Zhang, Shun Zhang, Matthew H. Tong, Yuchen Cui + 3 more
Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses…
Shoichiro Yamaguchi, Honda Naoki, Muneki Ikeda, Yuki Tsukada + 3 more
Animals are able to flexibly adapt to new environments by controlling different behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals’ decision-making, but methods available for quantifying such behavioral strategies have not been fully established.…
Joar Skalse, Alessandro Abate
The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy π. To do this, we need a model of how π relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to…
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov
'Ruslan Salakhutdinov'] Multi-task reinforcement learning (RL) aims to simultaneously learn policies for solving many tasks. Several prior works have found that relabeling past experience with different reward functions can improve sample efficiency. Relabeling methods typically ask: if, in hindsight, we assume that…
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving + 2 more
'Dario Amodei'] 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…
Chengyi Zhao, Yimin Wei, Junfeng Xiao, Yong Sun + 3 more
'Qiuquan Guo' 'Jun Yang'] The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used…
Hyunsoo Park, Sauradeep Majumdar, Xiaoqi Zhang, Jihan Kim + 1 more
The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Georgy Antonov, Peter Dayan
Exploration is vital for animals and artificial agents who face uncertainty about their environments due to initial ignorance or subsequent changes. Their choices need to balance exploitation of the knowledge already acquired, with exploration to resolve uncertainty [1, 2]. However, the exact algorithmic structure of…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…