Analysis of Reinforcement Learning Algorithms for Swarm Learning | A Rough Set Approach

Shamama Anwar

ISBN 10: 3659287318 / ISBN 13: 9783659287312

Edité par LAP Lambert Academic Publishing, 2012

Analysis of Reinforcement Learning Algorithms for Swarm Learning | A Rough Set Approach | Shamama Anwar | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659287312 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 106179985

Synopsis :

Reinforcement learning has received much attention in the past decades. The three forms of reinforcement learning algorithms are Actor Critic learning, Q learning and Reinforcement Comparison. Q-learning is a form of model-free reinforcement learning with one drawback that is the overestimation (Rising Q) problem. To solve this problem Rough Sets approach is used. This has lead to the modification of the traditional Q learning algorithms to a new form of Q learning namely, Rough Q learning. Actor Critic Learning have a separate memory structure to explicitly represent the policy independent of the value function. Another form of reinforcement learning is Reinforcement Comparison. Using reinforcement comparison method (RC), a reference reward is equated with an average of previously received rewards. The Actor Critic and RC method is also made better by using the rough set approach. The results of the study are in form of various plots for all three forms of reinforcement learning, their variations and the effect of temperature on them.

Présentation de l'éditeur: Reinforcement learning has received much attention in the past decades. The three forms of reinforcement learning algorithms are Actor Critic learning, Q learning and Reinforcement Comparison. Q-learning is a form of model-free reinforcement learning with one drawback that is the overestimation (Rising Q) problem. To solve this problem Rough Sets approach is used. This has lead to the modification of the traditional Q learning algorithms to a new form of Q learning namely, Rough Q learning. Actor Critic Learning have a separate memory structure to explicitly represent the policy independent of the value function. Another form of reinforcement learning is Reinforcement Comparison. Using reinforcement comparison method (RC), a reference reward is equated with an average of previously received rewards. The Actor Critic and RC method is also made better by using the rough set approach. The results of the study are in form of various plots for all three forms of reinforcement learning, their variations and the effect of temperature on them.

Détails bibliographiques

Titre : Analysis of Reinforcement Learning ...
Éditeur : LAP Lambert Academic Publishing
Date d'édition : 2012
Reliure : Taschenbuch
Etat : Neu