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Found 3 papers, 1 papers with code
Efficient Learning of High Level Plans from Play
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals.
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently accelerate reinforcement learning on complex tasks.
Planning from Pixels in Environments with Combinatorially Hard Search Spaces
The ability to form complex plans based on raw visual input is a litmus test for current capabilities of artificial intelligence, as it requires a seamless combination of visual processing and abstract algorithmic execution, two traditionally separate areas of computer science.
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