Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A fuzzy Actor-Critic reinforcement learning network
Information Sciences: an International Journal
Experimental analysis on Sarsa(λ) and Q(λ) with different eligibility traces strategies
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Theoretical advances of intelligent paradigms
Dynamic tuning of online data migration policies in hierarchical storage systems using reinforcement learning
A new mobile robot navigation method using fuzzy logic and a modified Q-learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy reinforcement learning approach to power control in wireless transmitters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Some results for dual hesitant fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The sequential and uncontrolled punishments in social life may lead to what psychologists call learned helplessness or depression. Like learning in social life, agents based on Fuzzy Reinforcement Learning FRL sometimes cannot learn well. Experiments show that if an agent continuously performs actions that cause sequential punishments in the beginning of learning, then it does not usually behave well and often selects actions that evoke punishments. Therefore, the learning takes so long or not successful. In this paper, we address this issue called faulty learning in RL algorithm by exploiting learned helplessness. We demonstrate learned helplessness in the training of an agent by FRL algorithm and analyze it. The result of analysis shows that since the action value function is approximated by a fuzzy system; continuous punishments lead all weight parameters of the approximator toward negative amounts. Hence, the agent cannot learn well. To prevent this problem, we propose a new reinforcement function. The proposed reinforcement function is adaptive and depends on the number of visit of the state. Simulation results show that new reinforcement function prevents learned helplessness and improves the learning in terms of learning speed and action quality. The proposed ideas can be used and extended to our social and psychology life.