Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies o...

Chen C. Intelligence moderates reinforcement learning: a minireview of the neural evidence. J Neurophysiol 113: 3459–3461, 2015. First published September 3, 2014; doi:10.1152/jn.00600.2014.—Our understanding of the neural basis of reinforcement learning and intelligence, two key factors contributing to human strivings, has progressed significantly recently. However, the overlap of these two li...

Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to ...

In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful mod...

We propose novel ways of solving Reinforcement Learning tasks (that is, stochastic optimal control tasks) by hybridising Evolutionary Algorithms with methods based on value functions. We call our approach Population-Based Reinforcement Learning. The key idea, from Evolutionary Computation, is that parallel interacting search processes (in this case Reinforcement Learning or Dynamic Programming ...

Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces, and cope with partial observability, evolutionary reinforcement-learning approaches have a strong ...

This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. Th...

Planning for autonomous vehicles is a challenging process that involves navigating through dynamic and unpredictable surroundings while making judgments in real-time. Traditional planning methods sometimes rely on predetermined rules or customized heuristics, which could not generalize well to various driving conditions. In this article, we provide unique framework enhance vehicle by fusing con...

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier envi...

In the Spoken Dialogue System literature, all studies consider the dialogue move as the unquestionable unit for reinforcement learning. Rather than learning at the dialogue move level, we apply the learning at the design level for three reasons : 1/ to alleviate the high-skill prerequisite for developers, 2/ to reduce the learning complexity by taking into account just the relevant subset of th...