5,087 research outputs found
Crawling in Rogue's dungeons with (partitioned) A3C
Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of
its gender. Rogue-like games are known for the necessity to explore partially
observable and always different randomly-generated labyrinths, preventing any
form of level replay. As such, they serve as a very natural and challenging
task for reinforcement learning, requiring the acquisition of complex,
non-reactive behaviors involving memory and planning. In this article we show
how, exploiting a version of A3C partitioned on different situations, the agent
is able to reach the stairs and descend to the next level in 98% of cases.Comment: Accepted at the Fourth International Conference on Machine Learning,
Optimization, and Data Science (LOD 2018
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Planning for many manipulation tasks, such as using tools or assembling
parts, often requires both symbolic and geometric reasoning. Task and Motion
Planning (TAMP) algorithms typically solve these problems by conducting a tree
search over high-level task sequences while checking for kinematic and dynamic
feasibility. This can be inefficient as the width of the tree can grow
exponentially with the number of possible actions and objects. In this paper,
we propose a novel approach to TAMP that relaxes discrete-and-continuous TAMP
problems into inference problems on a continuous domain. Our method, Stein Task
and Motion Planning (STAMP) subsequently solves this new problem using a
gradient-based variational inference algorithm called Stein Variational
Gradient Descent, by obtaining gradients from a parallelized differentiable
physics simulator. By introducing relaxations to the discrete variables,
leveraging parallelization, and approaching TAMP as an Bayesian inference
problem, our method is able to efficiently find multiple diverse plans in a
single optimization run. We demonstrate our method on two TAMP problems and
benchmark them against existing TAMP baselines.Comment: 14 pages, 9 figures, Learning Effective Abstractions for Planning
(LEAP) Workshop at CoRL 202
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