Search Results for author:
Found 16 papers, 6 papers with code
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents
To this end, we propose four different policy fusion methods for combining pre-trained policies.
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games
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.
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments
We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games.
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy 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).
Process Discovery for Structured Program Synthesis
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
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).
Fluid Dynamics
Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity
Image captioning as a multimodal task has drawn much interest in recent years.
Direct shape optimization through deep reinforcement learning
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
What is needed for simple spatial language capabilities in VQA?
Visual question answering (VQA) comprises a variety of language capabilities.
A review on Deep Reinforcement Learning for Fluid Mechanics
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
Accelerating Deep Reinforcement Learning of Active Flow Control strategies through a multi-environment approach
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.
Computational Physics
The meaning of "most" for visual question answering models
The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.
How clever is the FiLM model, and how clever can it be?
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.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
Deep learning evaluation using deep linguistic processing
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.
ShapeWorld - A new test methodology for multimodal language understanding
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities.
