Automatic feature identification in least-squares policy iteration using the Koopman operator framework

Dimensions overall score 3.0

• The KAE-LSPI algorithm enables automatic feature learning via the Koopman autoencoder (KAE) framework by reformulating least-squares fixed-point approximation in terms of extended dynamic mode decomposition (EDMD).method 95%
  Evidencepartial

  The KAE-LSPI algorithm is based on reformulating the so-called least-squares fixed-point approximation method in terms of extended dynamic mode decomposition (EDMD), thereby enabling automatic feature learning via the Koopman autoencoder (KAE) framework.

  Implicationpartial

  This is a core methodological contribution explicitly stated in the abstract and introduction.

  Verificationpartial

  partial

• The KAE-LSPI algorithm does not require pre-fixing features or kernels, unlike classical LSPI and KLSPI.method 95%
  Evidencepartial

  Unlike previous works, no features or kernels need to be fixed a priori in our approach.

  Implicationpartial

  This is a key advantage highlighted in the abstract and introduction, differentiating it from previous methods.

  Verificationpartial

  partial

• The number of features learned by the KAE technique in KAE-LSPI remains reasonable compared to the fixed number of features in classical LSPI.result 90%
  Evidencepartial

  Empirical results show the number of features learned by the KAE technique remains reasonable compared to those fixed in the classical LSPI algorithm.

  Implicationpartial

  This is an empirical result directly stated in the abstract and supported by the comparison in the chain walk problem.

  Verificationpartial

  partial

• The convergence of KAE-LSPI to an optimal or near-optimal policy is comparable to classical LSPI and KLSPI.result 90%
  Evidencepartial

  The convergence to an optimal or a near-optimal policy is also comparable to the other two methods.

  Implicationpartial

  This is an empirical result stated in the abstract and discussed in the context of the chain walk and inverted pendulum problems.

  Verificationpartial

  partial

• The KAE-LSPI algorithm was evaluated on stochastic chain walk and inverted pendulum control problems.method 95%
  Evidencepartial

  We compare the KAE-LSPI algorithm with two previous works, the classical least-squares policy iteration (LSPI) and the kernel-based least-squares policy iteration (KLSPI), using stochastic chain walk and inverted pendulum control problems as examples.

  Implicationpartial

  These are the specific benchmark problems used for evaluation, mentioned in the abstract and detailed in the experimental sections.

  Verificationpartial

  partial

• For the chain walk problem with 20 states, the KAE-LSPI algorithm learned 15 features.result 90%
  Evidencepartial

  Learned featuresk 15

  Implicationpartial

  This is a specific quantitative result from the experiments, presented in Table I.

  Verificationpartial

  partial

• For the chain walk problem with 50 states, the KAE-LSPI algorithm learned 45 features.result 90%
  Evidencepartial

  Learned featuresk 45

  Implicationpartial

  This is a specific quantitative result from the experiments, presented in Table I.

  Verificationpartial

  partial

• The KLSPI algorithm was not compared for the inverted pendulum control problem due to significant computation requirements related to data regeneration.limitation 90%
  Evidencepartial

  For this problem we compared only the performance of the LSPI and KAE-LSPI algorithms, as the KLSPI algorithm requires significant computation due to the data regeneration.

  Implicationpartial

  This is a limitation or reason for exclusion of a method in a specific experiment, stated in the experimental setup.

  Verificationpartial

  partial

Competitive landscape

A novel reinforcement learning algorithm that automatically learns features for policy iteration, removing the need for manual feature engineering.