Classifying global state preparation via deep reinforcement learning
نویسندگان
چکیده
منابع مشابه
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learning—the options framework—with deep Q-networks (DQNs) through the use of different “option heads” on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a...
متن کاملShared Autonomy via Deep Reinforcement Learning
In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user’s policy given their goal, and the set of possible goals the user might ...
متن کاملInverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. Inco...
متن کاملStrategic Dialogue Management via Deep Reinforcement Learning
Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the beh...
متن کاملLearning State Representations for Query Optimization with Deep Reinforcement Learning
Deep reinforcement learning is quickly changing the field of artificial intelligence. These models are able to capture a high level understanding of their environment, enabling them to learn difficult dynamic tasks in a variety of domains. In the database field, query optimization remains a difficult problem. Our goal in this work is to explore the capabilities of deep reinforcement learning in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning: Science and Technology
سال: 2020
ISSN: 2632-2153
DOI: 10.1088/2632-2153/abc81f