AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Reinforcement Learning with Action Discovery

The design of reinforcement learning solutions to many problems artificially constrain the action set available to an agent, in order to limit the exploration/sample complexity. While exploring, if an agent can discover new actions that can break through the constraints of its basic/atomic action set, then the quality of the learned decision policy could improve. On the flipside, considering al...

متن کامل

Deep Reinforcement Learning with POMDPs

Recent work has shown that Deep Q-Networks (DQNs) are capable of learning human-level control policies on a variety of different Atari 2600 games [1]. Other work has looked at treating the Atari problem as a partially observable Markov decision process (POMDP) by adding imperfect state information through image flickering [2]. However, these approaches leverage a convolutional network structure...

متن کامل

Reinforcement Learning with Deep Architectures

There is both theoretical and empirical evidence that deep architectures may be more appropriate than shallow architectures for learning functions which exhibit hierarchical structure, and which can represent high level abstractions. An important development in machine learning research in the past few years has been a collection of algorithms that can train various deep architectures effective...

متن کامل

Deep Reinforcement Learning with Double Q-Learning

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether this harms performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-le...

متن کامل

Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm

: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...

متن کامل

ذخیره 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: IEEE Transactions on Neural Networks and Learning Systems

سال: 2020

ISSN: 2162-237X,2162-2388

DOI: 10.1109/tnnls.2019.2942951