Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning

Nicolas Castanet, Olivier Sigaud, sylvain lamprier

Keywords: Exploration, Goal-conditioned Policies, Automatic curriculum, Stein Variational Gradient Descent

Abstract: Multi-goal Reinforcement Learning has recently attracted a large amount of research interest. By allowing experience to be shared between related training tasks, this setting favors generalization for new tasks at test time, whenever some smoothness exists in the considered representation space of goals. However, in settings with discontinuities in state or goal spaces (e.g. walls in a maze), a majority of goals are difficult to reach, due to the sparsity of rewards in the absence of expert knowledge. This implies hard exploration, for which some curriculum of goals must be discovered, to help agents learn by adapting training tasks to their current capabilities. We propose a novel approach: Stein Variational Goal Generation (SVGG), which builds on recent automatic curriculum learning techniques for goal-conditioned policies. SVGG seeks at preferably sampling new goals in the zone of proximal development of the agent, by leveraging a learned model of its abilities and a goal distribution modeled as particles in the exploration space. Our approach relies on Stein Variational Gradient Descent to dynamically attract the goal sampling distribution in areas of appropriate difficulty. We demonstrate the performances of the approach, in terms of success coverage in the goal space, compared to recent state-of-the-art RL methods for hard exploration problems.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)