Hence, with largestate and action spaces, it is essential to identify and exploit any possible structure existing in the system dynamics and reward function so as to minimize exploration phases and in turn reduce regret to reasonable values. Modern RL algorithms actually implicitly impose some structural properties either in the model parameters (transition probabilities and reward function, see e.g.

Reinforcement learning is well suited for optimizing policies of recommender systems.

However,most existing studies concentrate onthetext-to-image synthesis [11-14], which generates images from text descriptions without the original image.

While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g.

Another challenge ishowtoefficiently learn the semantic parser from denotations.

We assume, withoutlossofgenerality, that isdiagonal. The function m anditsderivativem0 arewell-definedandpositiveatz =0 (andhenceR ( ) is well-definedforall > ).

In this work, we conduct a comprehensive study of NetHack and examine various learning mechanisms to enhance the performance of neural models.

Neural Information Processing Systems http://nips.cc/