Reinforcement Learning Algorithms with Python

Stability and reproducibility

Stability and reproducibility are somehow interconnected with each other as the goal is to design an algorithm that is capable of consistency across multiple runs and that is not too invariant to small tweaks. For example, the algorithm shouldn't be too sensitive to changes in the values of the hyperparameters.

The main factor that makes deep RL algorithms difficult to replicate is intrinsic to the nature of deep neural networks. This is mainly due to random initialization of the deep neural networks and the stochasticity of optimization. Moreover, this situation is exacerbated in RL, considering that the environments are stochastic. Combined, these factors are also to the detriment of the interpretability of ...