Results 61 to 70 of about 311 (117)

Inverse Reinforcement Learning with Missing Data

CoRR, 2019
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are ...
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet   +3 more
openaire   +2 more sources

Stable Inverse Reinforcement Learning: Policies From Control Lyapunov Landscapes

IEEE Open Journal of Control Systems
Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborative control settings.
SAMUEL TESFAZGI, Leonhard Sprandl, Armin Lederer, Sandra Hirche   +3 more
doaj   +1 more source

The Legibility Methods in Agent Systems [PDF]

ITM Web of Conferences
To tackle problems in the domains of human-machine collaboration and multiagent cooperation (e.g., multiagent sequential decision-making, path planning, and navigation), “legibility” — the agent’s ability to convey its intentions through its behavior ...
Li Siyuan
doaj   +1 more source

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning

Scientific Reports
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
Chengyi Zhao, Yimin Wei, Junfeng Xiao, Yong Sun, Dongxing Zhang, Qiuquan Guo, Jun Yang   +6 more
doaj   +1 more source

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

AI
Background/Objectives: The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure.
Abhijeet Sahu, Venkatesh Venkatramanan, Richard Macwan   +2 more
doaj   +1 more source

Bayesian Nonparametric Inverse Reinforcement Learning [PDF]

, 2012
Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set.
Bernard Michini, Jonathan P. How
openaire   +3 more sources

Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion

Applied Sciences
In this paper, a novel reinforcement learning method that enables a humanoid robot to learn bipedal walking using a simple reference motion is proposed.
Naoya Itahashi, Hideaki Itoh, Hisao Fukumoto, Hiroshi Wakuya   +3 more
doaj   +1 more source

Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice

Paladyn, 2018
Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). IRL extracts an unknown reward function from samples of observed behaviour. Traditional IRL systems require large datasets to
Bhattacharyya Rupam, Hazarika Shyamanta M.   +1 more
doaj   +1 more source

The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning.

PLoS Biology, 2020
While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented.
Anis Najar, Emmanuelle Bonnet, Bahador Bahrami, Stefano Palminteri   +3 more
doaj   +1 more source

Non-Cooperative Inverse Reinforcement Learning

CoRR, 2019
Making decisions in the presence of a strategic opponent requires one to take into account the opponent's ability to actively mask its intended objective. To describe such strategic situations, we introduce the non-cooperative inverse reinforcement learning (N-CIRL) formalism.
Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar   +3 more
openaire   +3 more sources