Optimizing for control in Stochastic Environments

This code base is the portrayal of a model-based reinforcement learning algorithm for stochastic environments.

More specifically, we build a model-based RL algorithm which uses a Neural SDE-based model of the environment for state transition prediction, and from there uses Model-based policy optimization for training the agent in the stochastic environment.