A Policy Grad Grad Grad Grad ient Reinforcement Learning Algorithm with Fuzzy Function Approximation

For complex systems, reinforcement learning has to be generalised from a discrete form to a continuous form due to large state or action spaces. In this paper, the generalisation of reinforcement learning to continuous state space is investigated by using a policy gradient approach. Fuzzy logic is used as a function approximation in the generalisation. To guarantee learning convergence, a policy approximator and a state action value approximator are employed for the reinforcement learning. Both of them are based on fuzzy logic. The convergence of the learning algorithm is justified.