Abstract
We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD((Formula presented.))), we propose to use the forgetting factor commonly used in gradient-based or least-square estimation, and we show that it has a similar role as eligibility traces. An instrumental variable perspective is adopted to formulate the new algorithm, referred to as RLS-TD with forgetting factor (RLS-TD-f). An interesting aspect of the proposed algorithm is that it has an interpretation of a minimizer of an appropriate cost function. We test the effectiveness of the algorithm in a Policy Iteration setting, meaning that we aim to improve the performance of an initially stabilizing control policy (over large portion of the state space). We take a cart-pole benchmark and an adaptive cruise control benchmark as experimental platforms.
Original language | English |
---|---|
Pages (from-to) | 334-353 |
Journal | International Journal of Adaptive Control and Signal Processing |
Volume | 36 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2022 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- eligibility traces
- instrumental variable method
- least squares
- reinforcement learning
- temporal difference