A new framework for robot block-stacking tasks using causal probabilistic models.
arXiv research
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Dealing with sparse rewards is a longstanding challenge in reinforcement learning. The recent use of hindsight methods have achieved success on a variety of sparse-reward tasks, but they fail on complex tasks such as stacking multiple blocks with a robot arm in simulation. Curiosity-driven exploration using the predict…
Max entropy exploration guides reinforcement learning agents to pursue achievable goals.
LoCo learns local representations without end-to-end synchronization, improving performance on complex tasks.
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before. We present object-centric perception, pred…
New method automates asymmetric choice for better skill transfer in reinforcement learning.