To this end, we propose four different policy fusion methods for combining pre-trained policies.
Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environments.
We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games.
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL).
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL).
Image captioning as a multimodal task has drawn much interest in recent years.
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements.
Computational Engineering, Finance, and Science
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
In the case of AFC trained with Computational Fluid Mechanics (CFD) data, it was found that the CFD part, rather than the training of the Artificial Neural Network, was the limiting factor for speed of execution.
The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.
The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture.
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice.
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities.