3,588 research outputs found
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
Neural network ensembles: Evaluation of aggregation algorithms
Ensembles of artificial neural networks show improved generalization
capabilities that outperform those of single networks. However, for aggregation
to be effective, the individual networks must be as accurate and diverse as
possible. An important problem is, then, how to tune the aggregate members in
order to have an optimal compromise between these two conflicting conditions.
We present here an extensive evaluation of several algorithms for ensemble
construction, including new proposals and comparing them with standard methods
in the literature. We also discuss a potential problem with sequential
aggregation algorithms: the non-frequent but damaging selection through their
heuristics of particularly bad ensemble members. We introduce modified
algorithms that cope with this problem by allowing individual weighting of
aggregate members. Our algorithms and their weighted modifications are
favorably tested against other methods in the literature, producing a sensible
improvement in performance on most of the standard statistical databases used
as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa
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