RL for Planning and Planning for RL

The figure above illustrates the method:(a) Goal-conditioned RL often fails to reach distant goals, but can successfully reach the goal if starting nearby (inside the green region). Reinforcement learning (RL) has seen a lot of progress over the past few years, tackling increasingly complex tasks. Much of this progress has been enabled by combining existing RL algorithms with powerful function approximators (i.e., neural networks). Function approximators have enabled researchers to apply RL to tasks with high-dimensional inputs without hand-crafting representations, distance metrics, or low-level controllers. However, function approximators have not come for free, and their cost is reflected in notoriously challenging optimization: deep RL algorithms are famously unstable and sensitive to hyperparameters.