Least-Squares Reinforcement Learning Methods

2012 ◽  
Keyword(s):  
Reinforcement Learning ◽  
Least Squares ◽  
Learning Methods

Least-Squares Reinforcement Learning Methods

2011 ◽  
pp. 595-600
Author(s):  
Geoffrey I. Webb ◽  
Claude Sammut ◽  
Claudia Perlich ◽  
Tamás Horváth ◽  
Stefan Wrobel ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Least Squares ◽  
Learning Methods

Adaptive stock trading strategies with deep reinforcement learning methods

2020 ◽  
Vol 538 ◽  
pp. 142-158 ◽  
Author(s):  
Xing Wu ◽  
Haolei Chen ◽  
Jianjia Wang ◽  
Luigi Troiano ◽  
Vincenzo Loia ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategies ◽  
Stock Trading ◽  
Learning Methods

scholarly journals A proposal of privacy preserving reinforcement learning for secure multiparty computation

2017 ◽  
Vol 6 (2) ◽  
pp. 57 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Noritaka Shigei ◽  
Syunki Makino ◽  
Hiromi Miyajima ◽  
Yohtaro Miyanishi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Distributed Processing ◽  
Data Encryption ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Learning Methods ◽  
Q Learning ◽  
Encryption And Decryption ◽  
Maze Problem ◽  
Learning Data

Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

scholarly journals Generalized Representation Learning Methods for Deep Reinforcement Learning

2020 ◽  
Author(s):  
Hanhua Zhu
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Representation Learning ◽  
Learning Methods ◽  
New Methods ◽  
Compact State

Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.

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