J
Jiri Kubalik
Researcher at Czech Technical University in Prague
Publications - 17
Citations - 106
Jiri Kubalik is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: Symbolic regression & Reinforcement learning. The author has an hindex of 6, co-authored 13 publications receiving 79 citations.
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Proceedings ArticleDOI
Symbolic method for deriving policy in reinforcement learning
TL;DR: A novel method based on genetic programming is proposed to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived, which outperforms the standard policy derivation method.
Proceedings ArticleDOI
Reinforcement Learning with Symbolic Input-Output Models
TL;DR: Results show that the proposed NARX (nonlinear autoregressive with exogenous input) type models can reliably determine a good control policy based on a symbolic input-output process model and value function.
Proceedings ArticleDOI
Enhanced Symbolic Regression Through Local Variable Transformations
TL;DR: A GP extension introducing a new concept of local transformed variables, based on a locally applied affine transformation of the original variables is presented, which confirms the hypothesis that the transformed variables significantly improve the performance of the standard SNGP algorithm.
Journal ArticleDOI
SymFormer: End-to-end symbolic regression using transformer-based architecture
TL;DR: This work proposes a transformer-based approach called SymFormer, which predicts the formula by outputting the individual symbols and the corresponding constants simultaneously simultaneously, which leads to better performance in terms of available data.
Journal ArticleDOI
Policy Derivation Methods for Critic-Only Reinforcement Learning in Continuous Action Spaces
TL;DR: Several variants of the policy-derivation algorithm are introduced and compared on two continuous state-action benchmarks: double pendulum swing-up and 3D mountain car.