Reinforcement Learning to Train Agents to Play Games

Lamba, Vishal (2019) Reinforcement Learning to Train Agents to Play Games. Masters thesis, Indian Institute Of Science Education and Research Kolkata.

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This thesis focuses on the challenge of decoupling state perception and function approx- imation when applying Deep Learning Methods within Reinforcement Learning. As a starting point, high-dimensional states were considered, this being the fundamental lim- itation when applying Reinforcement Learning to real world tasks. Different approaches to solve the problem of estimation Q-values of states in an en- vironment are discussed. The key idea is to estimating Q-values for large state-spaces, where tabular approaches are no longer feasible. As a mean to perform Q-function ap- proximation, we search for supervised learning methods within Deep Learning. Using python, a model for the environment of the game of 'Pong' was created and using Deep Q-Learning an agent was trained to achieve excellent performance at playing the game.

Item Type: Thesis (Masters)
Additional Information: Supervisor: Prof. P.K. Panigrahi
Uncontrolled Keywords: Deep Learning Methods; Deep Neural Networks; Function Approximation; Reinforcement Learning; State Perception
Subjects: Q Science > QA Mathematics
Divisions: Department of Mathematics and Statistics
Depositing User: IISER Kolkata Librarian
Date Deposited: 09 Oct 2019 09:05
Last Modified: 09 Oct 2019 09:05

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