The RL agent traverses the latent space by adding Gaussian noise to its current position with a mean value determined by the policy network. The parameter σ in the covariance matrix controls the agent's exploration-exploitation trade-off with larger values allowing more exploration of the space while lower values are resulting in a more local search. We reduce exploration over time to allow convergence of the agent's loss function by introducing a decaying factor γ for the standard deviation σ with being set to γ = 0.9995 for all experiments.