Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks. (arXiv:2206.01812v1 [cs.LG])

Deep reinforcement learning has shown promise in discrete domains requiring
complex reasoning, including games such as Chess, Go, and Hanabi. However, this
type of reasoning is less often observed in long-horizon, continuous domains
with high-dimensional observations, where instead RL research has predominantly
focused on problems with simple high-level structure (e.g. opening a drawer or
moving a robot as fast as possible). Inspired by combinatorially hard
optimization problems, we propose a set of robotics tasks which admit many
distinct solutions at …

arxiv challenges deep rl rl