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JMLR
2012
10 years 2 months ago
Contextual Bandit Learning with Predictable Rewards
Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
Alekh Agarwal, Miroslav Dudík, Satyen Kale,...
AGI
2011
11 years 3 months ago
Reinforcement Learning and the Bayesian Control Rule
We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
Pedro Alejandro Ortega, Daniel Alexander Braun, Si...
CORR
2011
Springer
136views Education» more  CORR 2011»
11 years 3 months ago
Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments
In this paper, we confront the problem of applying reinforcement learning to agents that perceive the environment through many sensors and that can perform parallel actions using ...
Enric Celaya, Josep M. Porta
NECO
2010
103views more  NECO 2010»
11 years 10 months ago
Posterior Weighted Reinforcement Learning with State Uncertainty
Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn f...
Tobias Larsen, David S. Leslie, Edmund J. Collins,...
JIRS
2010
120views more  JIRS 2010»
11 years 10 months ago
Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning
Distributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems ...
Laëtitia Matignon, Guillaume J. Laurent, Nadi...
ICRA
2010
IEEE
137views Robotics» more  ICRA 2010»
11 years 10 months ago
Robot reinforcement learning using EEG-based reward signals
Abstract— Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These r...
Iñaki Iturrate, Luis Montesano, Javier Ming...
NN
2007
Springer
105views Neural Networks» more  NN 2007»
11 years 11 months ago
Guiding exploration by pre-existing knowledge without modifying reward
Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many app...
Kary Främling
CORR
1998
Springer
164views Education» more  CORR 1998»
11 years 11 months ago
Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorit...
Aristidis Likas, Isaac E. Lagaris
AAMAS
2002
Springer
11 years 11 months ago
Relational Reinforcement Learning for Agents in Worlds with Objects
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it ...
Saso Dzeroski
AIHC
2007
Springer
12 years 6 months ago
Emotion and Reinforcement: Affective Facial Expressions Facilitate Robot Learning
Computer models can be used to investigate the role of emotion in learning. Here we present EARL, our framework for the systematic study of the relation between emotion, adaptation...
Joost Broekens
books