Results 71 to 80 of about 88,537 (171)

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

Inverse Reinforcement Learning in Swarm Systems

, 2017
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems.
KhudaBukhsh, Wasiur R., Koeppl, Heinz, Zoubir, Abdelhak M., Šošić, Adrian   +3 more
core  

Hybrid Inverse Reinforcement Learning

The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations with more robustness to error compounding than behavioral cloning approaches.
Ren, Juntao, Swamy, Gokul, Wu, Zhiwei Steven, Bagnell, J. Andrew, Choudhury, Sanjiban   +4 more
openaire   +2 more sources

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

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

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

Receding Horizon Inverse Reinforcement Learning

, 2022
Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizon inverse reinforcement learning (RHIRL), a new IRL algorithm for high-dimensional, noisy, continuous systems with black-box dynamic models.
Xu, Yiqing, Gao, Wei, Hsu, David
openaire   +2 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

Probabilistic inverse reinforcement learning in unknown environments

, 2013
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to ...
Dimitrakakis, Christos, Tossou, Aristide
core   +1 more source

Non-Cooperative Inverse Reinforcement Learning

, 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.
Zhang, Xiangyuan, Zhang, Kaiqing, Miehling, Erik, Başar, Tamer   +3 more
openaire   +2 more sources