Results 41 to 50 of about 88,537 (171)

Inverse Reinforcement Learning with Constraint Recovery

, 2023
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.
Das, Nirjhar, Chattopadhyay, Arpan
openaire   +2 more sources

Interactive Teaching Algorithms for Inverse Reinforcement Learning

, 2019
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of ...
Cevher, Volkan, Devidze, Rati, Kamalaruban, Parameswaran, Singla, Adish   +3 more
core   +1 more source

Repeated Inverse Reinforcement Learning

, 2017
The first two authors contributed equally to this work.
Amin, Kareem, Jiang, Nan, Singh, Satinder   +2 more
openaire   +2 more sources

A Systematic Study on Reinforcement Learning Based Applications

Energies, 2023
We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems ...
Keerthana Sivamayil, Elakkiya Rajasekar, Belqasem Aljafari, Srete Nikolovski, Subramaniyaswamy Vairavasundaram, Indragandhi Vairavasundaram   +5 more
doaj   +1 more source

OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning

, 2017
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks.
Bacon, Pierre-Luc, Chang, Wei-Di, Henderson, Peter, Meger, David, Pineau, Joelle, Precup, Doina   +5 more
core   +1 more source

Bounded Low Latency via Inverse Reinforcement Learning

IEEE Access
Accurate traffic prediction is essential for effective resource utilization and improving user experience quality in next generation wireless networks.
Hossein Shafieirad, Raviraj S. Adve, Akram Bin Sediq, Hamza U. Sokun   +3 more
doaj   +1 more source

A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG

Biomimetics, 2023
In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control ...
Yanbiao Li, Zhao Chen, Chentao Wu, Haoyu Mao, Peng Sun   +4 more
doaj   +1 more source

Trust-Region Inverse Reinforcement Learning

IEEE Transactions on Automatic Control
This paper proposes a new unified inverse reinforcement learning (IRL) framework based on trust-region methods and a recently proposed Pontryagin differential programming (PDP) method in Jin et al. (2020), which aims to learn the parameters in both the system model and the cost function for three types of problems, namely, N-player nonzero-sum ...
Kun Cao, Lihua Xie
openaire   +2 more sources

Gamma-Regression-Based Inverse Reinforcement Learning From Suboptimal Demonstrations

IEEE Access
Inverse reinforcement learning (IRL) is a technique that estimates the intention of an expert who acts optimally on a specific intention, as a reward from demonstration (i.e., recorded data of the expert’s behavior). Traditional IRL algorithms are
Daiko Kishikawa, Sachiyo Arai
doaj   +1 more source

Paraphrase Generation with Deep Reinforcement Learning

, 2018
Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP), and plays a key role in a number of applications such as question answering, search, and dialogue.
Jiang, Xin, Li, Hang, Li, Zichao, Shang, Lifeng   +3 more
core   +1 more source