TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Projections for Approximate Policy Iteration Algorithms

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko36th International Conference on Machine Learning, ICML 2019
KustantajaPMLR
Sivut267-276
ISBN (elektroninen)9781510886988
TilaJulkaistu - kesäkuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Machine Learning - Long Beach, Yhdysvallat
Kesto: 9 kesäkuuta 201915 kesäkuuta 2019

Conference

ConferenceInternational Conference on Machine Learning
MaaYhdysvallat
KaupunkiLong Beach
Ajanjakso9/06/1915/06/19

Tiivistelmä

Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requires to constrain the change in action distribution. Several approximations exist in the literature to solve this constrained policy update problem. In this paper, we propose to improve over such solutions by introducing a set of projections that transform the constrained problem into an unconstrained one which is then solved by standard gradient descent. Using these projections, we empirically demonstrate that our approach can improve the policy update solution and the control over exploration of existing approximate policy iteration algorithms.

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