Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via
  Latent Model Ensembles   Learning complex robot behaviors through interaction requires structured
exploration. Planning should target interactions with the potential to optimize
long-term performance, while only reducing uncertainty where conducive to this
objective. This paper presents Latent Optimistic Value Exploration (LOVE), a
strategy that enables deep exploration through optimism in the face of
uncertain long-term rewards. We combine latent world models with value function
estimation to predict infinite-horizon returns and recover associated
uncertainty via ensembling. The policy is then trained on an upper confidence
bound (UCB) objective to identify and select the interactions most promising to
improve long-term performance. We apply LOVE to visual robot control tasks in
continuous action spaces and demonstrate on average more than 20% improved
sample efficiency in comparison to state-of-the-art and other exploration
objectives. In sparse and hard to explore environments we achieve an average
improvement of over 30%.

18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys