19,844 research outputs found
Multi-objective Reinforcement Learning
In this talk we present PQ-learning, a new Reinforcement Learning (RL) algorithm that
determines the rational behaviours of an agent in multi-objective domainsThis work is partially funded by: grant TIN2009-14179 (Spanish Government, Plan Nacional de I+D+i)
and Universidad de Málaga, Campus de Excelencia Internacional AndalucĂa Tech. Manuela Ruiz-Montiel
is funded by the Spanish Ministry of Education through the National F.P.U. Progra
Using Collective Intelligence to Route Internet Traffic
A COllective INtelligence (COIN) is a set of interacting reinforcement
learning (RL) algorithms designed in an automated fashion so that their
collective behavior optimizes a global utility function. We summarize the
theory of COINs, then present experiments using that theory to design COINs to
control internet traffic routing. These experiments indicate that COINs
outperform all previously investigated RL-based, shortest path routing
algorithms.Comment: 7 page
Difference of Convex Functions Programming Applied to Control with Expert Data
This paper reports applications of Difference of Convex functions (DC)
programming to Learning from Demonstrations (LfD) and Reinforcement Learning
(RL) with expert data. This is made possible because the norm of the Optimal
Bellman Residual (OBR), which is at the heart of many RL and LfD algorithms, is
DC. Improvement in performance is demonstrated on two specific algorithms,
namely Reward-regularized Classification for Apprenticeship Learning (RCAL) and
Reinforcement Learning with Expert Demonstrations (RLED), through experiments
on generic Markov Decision Processes (MDP), called Garnets
Ensemble Kalman Filter (EnKF) for Reinforcement Learning (RL)
This paper is concerned with the problem of representing and learning the
optimal control law for the linear quadratic Gaussian (LQG) optimal control
problem. In recent years, there is a growing interest in re-visiting this
classical problem, in part due to the successes of reinforcement learning (RL).
The main question of this body of research (and also of our paper) is to
approximate the optimal control law {\em without} explicitly solving the
Riccati equation. For this purpose, a novel simulation-based algorithm, namely
an ensemble Kalman filter (EnKF), is introduced in this paper. The algorithm is
used to obtain formulae for optimal control, expressed entirely in terms of the
EnKF particles. For the general partially observed LQG problem, the proposed
EnKF is combined with a standard EnKF (for the estimation problem) to obtain
the optimal control input based on the use of the separation principle. A
nonlinear extension of the algorithm is also discussed which clarifies the
duality roots of the proposed EnKF. The theoretical results and algorithms are
illustrated with numerical experiments
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