MohammadJavad Davari Dolatabadi - MSc Student, Department of Mechatronics Eng., Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Khalil Alipour - Assistant professor, Department of Mechatronics Eng., Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Alireza Hadi - Assistant professor, Department of Mechatronics Eng., Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

In this paper, we present three new methods to accelerate thelearning of a task by deterministic policy gradient algorithm.We focus specifically on learning of a task, which has theCredit Assignment (CA) problem. A Reinforcement Learning(RL) agent is performing this task in high dimensional statespace.The main idea of this paper is to use latent variablesthat deep autoencoders provide, to make a better rewardingsystem. We show that using these new rewards helpstremendously to learn the task in the similar circumstances.The task chosen for the algorithm is Push Recovery (PR) in asimulated environment.

Deep Learning- Push Recovery- CreditAssignment Problem- Latent Variable- Rewarding System-Inverse Reinforcement Learning