Search
Now showing items 1-10 of 792
Decentralized reinforcement learning applied to mobile robots
(Springer, 2017)
In this paper, decentralized reinforcement learning is applied to a control problem with a multidimensional action space. We propose a decentralized reinforcement learning architecture for a mobile robot, where the individual ...
A fast hybrid reinforcement learning framework with human corrective feedback
(Springer, 2019)
Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning ...
Reinforcement learning with restrictions on the action set
(SIAM Publications, 2015)
Consider a two-player normal-form game repeated over time. We introduce an
adaptive learning procedure, where the players only observe their own realized payoff at each stage.
We assume that agents do not know their own ...
Reinforcement and inference in cross-situational world learning
(Frontiers Research FoundationLausanne, 2013-11)
Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm ...
Speeding-up reinforcement learning through abstraction and transfer learning
(Saint Paul, Minnesota, 2013-05-10)
We are interested in the following general question: is it pos-
sible to abstract knowledge that is generated while learning
the solution of a problem, so that this abstraction can ac-
celerate the learning process? ...
Determinants of Instrumental extinction in terrestrial toads (Bufo arenarum)
(Elsevier, 2006-11)
Previous research in a water-reinforced instrumental training situation with toads (Bufo arenarum) has shown that performance in both acquisition and extinction is poorer after partial, rather than continuous reinforcement ...
Actions Combination Method for Reinforcement Learning
(2009-09-17)
The software agents are programs that can perceive from their environment and they act to reach their design goals.
In most cases the selected agent architecture determines its behaviour in response to different problem ...
Modelling shared attention through relational reinforcement learning
(SPRINGERDORDRECHT, 2012)
Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, ...
Reinforcement Learning using Gaussian Processes for Discretely Controlled Continuous Processes
(Plapiqui(uns-conicet), 2013-07)
In many application domains such as autonomous avionics, power electronics and process systems engineering there exist discretely controlled continuous processes (DCCPs) which constitute a special subclass of hybrid dynamical ...
Learning obstacle avoidance with an operant behavioral model
(Massachusetts Institute of Technology, 2004)
Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to ...