Memory augmented deep reinforcement learning in non-markovian environments = 비마르코프 환경에서의 메모리 결합 심층 강화학습

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 138
  • Download : 0
Artificial neural networks currently provide the best performance in the field of reinforcement learning. Here, a new deep reinforcement learning framework, called the deep recurrent external memory Q-Network (DReEMQN) is proposed. It consists of a deep neural network and an external memory matrix. The neural network acts as a function approximator for the deep Q-learning algorithm. The external memory is manipulated by a recurrent layer from with in the network, creating a true integrative framework for both learning and remembering what was learnt. The external memory is able to remember longer sequences of the observed states and actions taken by the reinforcement learning agent, and thus this framework is aimed at functioning well in real world environments where the environment state description is limited and long term dependencies on previous actions and states are needed to be remembered. DReEMQN is then tested on partially observable grid world environments of multiple sizes and is compared with a Deep Recurrent Q-Network, which does not have an external memory. The results obtained from the said experiments confirmed that the external memory integration in a deep reinforcement learning algorithm aids the algorithm to perform better in environments where observations are limited and an internal representation of the unobserved environment states and the agent’s past actions is needed.
Advisors
Kim, Jong Hwanresearcher김종환researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.8,[iv, 36 p. :]

Keywords

Machine learning▼adeep reinforcement learning▼aartificial neural network▼aattention mechanism; 기계 학습▼a심층 강화학습▼a인공 신경망; attention 메거니즘▼a비마르코프

URI
http://hdl.handle.net/10203/266740
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=828587&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0