Add to ORKGOne of the reinforcement learning algorithms proposed by Igarashi and Ishihara is a combining method of policy gradient method and fuzzy control. In 2012, M. Sugimoto implemented the algorithm to the RoboCup Small Size League action decision system. The system received 30 scenes, taken from RoboCup Japan Open 2012 Competition to be learned. The purpose of this paper is to present the detailed analysis on the fuzzy rules in the policies taken from the system in order to find out the cause of the failure in the learning of 5 of the scenes received. A method was proposed to determine the rules that caused error in the learning of 5 scenes by evaluating the degree of contribution and divergence of each rule
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A new fuzzy reinforcement learning algorithm that tunes the input and the output parameters of a fuz...
In this paper, a reinforcement learning algorithm is presented which is used to implement a fuzzy co...
In this study a gain scheduling method for the scaling factors of the input variables to the fuzzy l...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
A new fuzzy reinforcement learning algorithm that tunes the input and the output parameters of a fuz...
In this paper, a reinforcement learning algorithm is presented which is used to implement a fuzzy co...
In this study a gain scheduling method for the scaling factors of the input variables to the fuzzy l...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning ...
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized...