Results 141 to 150 of about 88,537 (171)

Learning strategies in table tennis using inverse reinforcement learning

Biological Cybernetics, 2014
Learning a complex task such as table tennis is a challenging problem for both robots and humans. Even after acquiring the necessary motor skills, a strategy is needed to choose where and how to return the ball to the opponent's court in order to win the game. The data-driven identification of basic strategies in interactive tasks, such as table tennis,
Mülling, K., Boularias, A., Mohler, B., Schölkopf, B., Peters, J.   +4 more
openaire   +4 more sources

Maximum Entropy Inverse Reinforcement Learning

2008
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of re- covering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based
Ziebart, Brian D., Maas, Andrew, J. Andrew Bagnell, Anind K. Dey   +3 more
openaire   +1 more source

Inverse reinforcement learning with evaluation

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006
Reinforcement learning (RL) is a method that helps programming an autonomous agent through human-like objectives as reinforcements, where the agent is responsible for discovering the best actions to fulfil the objectives. Nevertheless, it is not easy to disentangle human objectives in reinforcement like objectives.
V. Freire da Silva, A.H. Reali Costa, P. Lima   +2 more
openaire   +1 more source

Neuroevolution-Based Inverse Reinforcement Learning

2016
Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards.
openaire   +1 more source

Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
exaly  

Sophisticated Swarm Reinforcement Learning by Incorporating Inverse Reinforcement Learning

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023
Yasuaki Kuroe, Kenya Takeuchi
openaire   +1 more source

Inverse Reinforcement Learning

2011
Pieter Abbeel, Andrew Y. Ng
openaire   +1 more source

Relative Entropy Inverse Reinforcement Learning

2011
We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP).
Boularias, A., Kober, J., Peters, J.
openaire   +2 more sources

Obesity and adverse breast cancer risk and outcome: Mechanistic insights and strategies for intervention

Ca-A Cancer Journal for Clinicians, 2017
Cynthia Morata-Tarifa, Joyce M Slingerland   +1 more
exaly  

Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma

Ca-A Cancer Journal for Clinicians, 2020
Aaron J Grossberg, William L Hwang, Anirban Maitra   +2 more
exaly