Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-Based Reinforcement Learning via Latent-Space Collocation   The ability to plan into the future while utilizing only raw high-dimensional
observations, such as images, can provide autonomous agents with broad
capabilities. Visual model-based reinforcement learning (RL) methods that plan
future actions directly have shown impressive results on tasks that require
only short-horizon reasoning, however, these methods struggle on temporally
extended tasks. We argue that it is easier to solve long-horizon tasks by
planning sequences of states rather than just actions, as the effects of
actions greatly compound over time and are harder to optimize. To achieve this,
we draw on the idea of collocation, which has shown good results on
long-horizon tasks in optimal control literature, and adapt it to the
image-based setting by utilizing learned latent state space models. The
resulting latent collocation method (LatCo) optimizes trajectories of latent
states, which improves over previously proposed shooting methods for visual
model-based RL on tasks with sparse rewards and long-term goals. Videos and
code at https://orybkin.github.io/latco/.

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The categories of this paper are: physics
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat