'Practice' for Enhancing the Performance of a Deep Reinforcement Learning Agent

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Kancharla, V. S. S. R. T. (2019). 'Practice' for Enhancing the Performance of a Deep Reinforcement Learning Agent. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Deep reinforcement learning has demonstrated its capability to solve a diverse array of challenging problems, which were not able to solve previously. It has been able to achieve human-level performance in Atari 2600 games and it has shown great potential to self-driving cars, robotics, and natural language processing. However, it requires long training time to learn feature representations from raw sensory data and makes it difficult to use deep reinforcement learning in real-world applications. Transfer learning, specifically sim-to-real transfer, has been suggested to learn features but designing of new environments or defining an environment with similar or relevant goals requires careful human efforts. This thesis aims to leverage practice approaches, which do not require a new environmental design for transfer learning, to reduce the time for training a deep reinforcement learning agent and enhance its performance. First, this thesis introduces the use of practice approach applicable to end-to-end models with a deep reinforcement learning algorithm. It shows that the approach improves the performance of an agent and presents experimental results of this approach in complex environments. Second, this thesis presents a novel strategy, iterative practice, which repeats short period of practice and short period of learning until desired results are achieved. It presents experimental results and verifies that iterative practice improves learning. Last, it introduces a new strategy, shared experience for iterative practice, which reduces the interactions with an environment. It presents observations on this approach applied in complex environments and examines the effect of reduced interactions on learning.

Details
Author

Kancharla, Venkata Sai Santosh Ravi Teja

Title

'Practice' for Enhancing the Performance of a Deep Reinforcement Learning Agent

Physical Description

1 online resource (67 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Deep reinforcement learning has demonstrated its capability to solve a diverse array of challenging problems, which were not able to solve previously. It has been able to achieve human-level performance in Atari 2600 games and it has shown great potential to self-driving cars, robotics, and natural language processing. However, it requires long training time to learn feature representations from raw sensory data and makes it difficult to use deep reinforcement learning in real-world applications. Transfer learning, specifically sim-to-real transfer, has been suggested to learn features but designing of new environments or defining an environment with similar or relevant goals requires careful human efforts. This thesis aims to leverage practice approaches, which do not require a new environmental design for transfer learning, to reduce the time for training a deep reinforcement learning agent and enhance its performance. First, this thesis introduces the use of practice approach applicable to end-to-end models with a deep reinforcement learning algorithm. It shows that the approach improves the performance of an agent and presents experimental results of this approach in complex environments. Second, this thesis presents a novel strategy, iterative practice, which repeats short period of practice and short period of learning until desired results are achieved. It presents experimental results and verifies that iterative practice improves learning. Last, it introduces a new strategy, shared experience for iterative practice, which reduces the interactions with an environment. It presents observations on this approach applied in complex environments and examines the effect of reduced interactions on learning.

Genre

masters theses

Subjects--Topics

Artificial intelligence

Degree

M.S.

Keywords

Deep Learning
Machine Learning
Reinforcement Learning
Transfer Learning

Subject Area

Computer Science

Advisor(s)

Lee, Minwoo

Committee Members

Akella, Srinivas
Shin, Min

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2019.

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Rights Holder Information

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Identifier

Kancharla_uncc_0694N_12150

Permalink

http://hdl.handle.net/20.500.13093/etd:1988