Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

Understand

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions.

• To decide on an action to take, CEM conducts a search for the action sequence with the highest return according to the dynamics model and reward.
• Action sequences are typically randomly sampled from an unconditional Gaussian distribution and evaluated on the environment.
• This distribution is iteratively updated towards action sequences with higher returns.