Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2023

Abstract

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state using contextual key frames (CKFs), which can then be used to extract a function that represents the affordances of the state; in addition, two loss functions are introduced with respect to the affordances of the state. The novelty of the IECR framework lies in its capacity to extract contextual information from the environment and learn from the CKFs' representation. We validate the framework by developing four new algorithms that learn using context: Iota deep Q-network (IDQN), Iota double deep Q-network (IDDQN), Iota dueling deep Q-network (IDuDQN), and Iota dueling double deep Q-network (IDDDQN). Furthermore, we evaluate the framework and the new algorithms in five discrete environments. We show that all the algorithms, which use contextual information, converge in around 40 000 training steps of the neural networks, significantly outperforming their state-of-the-art equivalents.

Keywords

Artificial intelligence, deep reinforcement learning (RL), machine learning (ML, neural networks, Q-learning (QL)

Discipline

Artificial Intelligence and Robotics | OS and Networks | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Neural Networks and Learning Systems

First Page

1

Last Page

14

ISSN

2162-237X

Identifier

10.1109/TNNLS.2023.3325633

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNNLS.2023.3325633

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