Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning
Abstract
Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications. However, issues such as scalability, explainability, and Markovian assumptions limit its applicability in certain domains. We observe that many of these shortcomings emanate from the simulator as opposed to the RL training algorithms themselves. As such, we propose a semantic proxy for simulation based on a temporal extension to annotated logic. In comparison with two high-fidelity simulators, we show up to three orders of magnitude speed-up while preserving the quality of policy learned. In addition, we show the ability to model and leverage non-Markovian dynamics and instantaneous actions while providing an explainable trace describing the outcomes of the agent actions.
Cite
@article{arxiv.2310.06835,
title = {Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning},
author = {Kaustuv Mukherji and Devendra Parkar and Lahari Pokala and Dyuman Aditya and Paulo Shakarian and Clark Dorman},
journal= {arXiv preprint arXiv:2310.06835},
year = {2023}
}
Comments
Submitted to 2024 IEEE International Conference on Semantic Computing