Genetic network programming with reinforcement learning and optimal search component : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand
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Date
2019
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Massey University
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Abstract
This thesis presents ways of improving the genetic composition, structure and learning
strategies for a graph-based evolutionary algorithm, called Genetic Networking Programming
with Reinforcement Learning (GNP-RL), particularly when working with multi-agent and
dynamic environments. GNP-RL is an improvement over Genetic Programming, allowing for
the concise representation of solutions in terms of a networked graph structure and uses RL to
further refine the graph solutions. This work has improved GNP-RL by combining three new
techniques: Firstly, it has added a reward and punishment scheme as part of its learning strategy
that supports constraint conformance, allowing for a more adaptive training of the agent, so
that it can learn how to avoid unwanted situations more effectively. Secondly, an optimal
search algorithm has been combined in the GNP-RL core to get an accurate analysis of the
exploratory environment. Thirdly, a task prioritization technique has been added to the agent’s
learning by giving promotional rewards, so they are trained on how to take priority into account
when performing tasks. In this thesis, we applied the improved algorithm to the Tile World
benchmarking testbed, which is considered as one of the standard complex problems in this
domain, having only a sparse training set. Our experiment results show that the proposed
algorithm is superior than the best existing variant of the GNP-RL algorithm [1]. We have
achieved 86.66% test accuracy on the standard benchmarking dataset [2]. In addition, we have
created another benchmarking dataset, similar in complexity to the one proposed in [1], to test
the proposed algorithms further, where it achieved a test accuracy of 96.66%; that is 33.66%
more accurate.
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Keywords
Genetic algorithms, Evolutionary programming (Computer science), Graph algorithms, Querying (Computer science), Artificial intelligence