TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
arXiv:2401.11512v2 Announce Type: replace Abstract: Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information necessary for making optimal decisions. In order to address this problem, in this paper, we introduce the Transfer Entropy Redundancy Criterion (TERC), an information-theoretic criterion, which determines if there is \textit{entropy transferred} from observable state variables to actions during training. We define an algorithm based on TERC that provably excludes var
The paper leverages information theory, a well-established field, to address a critical and persistent challenge in developing more efficient and robust RL systems, building on recent advances in AI interpretability and efficiency.
This research provides a more principled and data-driven method for state variable selection in reinforcement learning, which could lead to significant improvements in training efficiency, model performance, and resource utilization for AI systems.
Reinforcement learning practitioners and researchers now have a provable, information-theoretic criterion (TERC) for selecting optimal state variables, potentially accelerating the development of more complex and autonomous AI agents.
- · AI agents developers
- · Reinforcement learning researchers
- · Data scientists
- · Robotics
- · Trial-and-error RL modelers
- · Inefficient AI systems
More efficient and interpretable reinforcement learning models will emerge.
This could enable the deployment of RL in more complex real-world scenarios with limited computational resources.
Improved efficiency in RL might accelerate advancements in autonomous systems across various industries, from logistics to scientific discovery.
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