  Quantum Reinforcement Learning (QRL) offers potential advantages over
classical Reinforcement Learning, such as compact state space representation
and faster convergence in certain scenarios. However, practical benefits
require further validation. QRL faces challenges like flat solution landscapes,
where traditional gradient-based methods are inefficient, necessitating the use
of gradient-free algorithms. This work explores the integration of
metaheuristic algorithms -- Particle Swarm Optimization, Ant Colony
Optimization, Tabu Search, Genetic Algorithm, Simulated Annealing, and Harmony
Search -- into QRL. These algorithms provide flexibility and efficiency in
parameter optimization. Evaluations in $5\times5$ MiniGrid Reinforcement
Learning environments show that, all algorithms yield near-optimal results,
with Simulated Annealing and Particle Swarm Optimization performing best. In
the Cart Pole environment, Simulated Annealing, Genetic Algorithms, and
Particle Swarm Optimization achieve optimal results, while the others perform
slightly better than random action selection. These findings demonstrate the
potential of Particle Swarm Optimization and Simulated Annealing for efficient
QRL learning, emphasizing the need for careful algorithm selection and
adaptation.
