Curious iLQR: Resolving Uncertainty in Model-based RL

Understand

Curiosity as a means to explore during reinforcement learning problems has recently become very popular.

• However, very little progress has been made in utilizing curiosity for learning control.
• In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
• During trajectory optimization the curious iLQR attempts to minimize both the task-dependent cost and the uncertainty in the dynamics model.