Research
- Deep Reinforcement Learning
- Multi-Agent Reinforcement Learning
- Multi-Agent Collaboration
Publications
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Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar
(2023).
Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy.
Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON).
ISSN 1553-572X.
Show summary
While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful
function approximators, they often suffer from inflexibility in
terms of deployment beyond the training scenario and also
include irrelevant data priors in the form of an ordered array of
input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research
covers methods that are trained on a fixed number of agents,
restricted by the fixed size of the input vector. In this paper,
we argue that this is not a reasonable assumption, both in
terms of the inflexible amount of environmental information
and the restrictive nature of the structure of the information.
We explore DeepSets and Set Transformers as two powerful set
function approximators to address the problem of cardinality
invariance and permutation invariance in the observation space of
a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of
the DeepSets and Set Transformer methods through simulated
experiments on a challenging multi-agent environment, that
otherwise yields sub-optimal policies through traditional function
approximation approaches. We demonstrate that both DeepSets
and Set Transformer based encoders scale well to increasing the
number of agents from training to evaluation.
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Published
Apr. 16, 2024 10:50 AM