In this paper we address reinforcement learning problems with continuous state-action spaces. We propose a new algorithm, tted natural actor-critic (FNAC), that extends the work in [1] to allow for general function approximation and data reuse. We combine the natural actor-critic architecture [1] with a variant of tted value iteration using importance sampling. The method thus obtained combines...

The study of intertextuality, the shaping of a text’s meaning by other texts, remains a laborious process for the literary critic. Kristeva (Kristeva, 1986) suggests that "Any text is constructed as a mosaic of quotations; any text is the absorption and transformation of another.& The nature of these mosaics is widely varied, from direct quotations representing a simple and overt intertextualit...

We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect overfitting. Finally, we...

We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this prob...

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For sup...