Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-Based Offline Planning   Offline learning is a key part of making reinforcement learning (RL) useable
in real systems. Offline RL looks at scenarios where there is data from a
system's operation, but no direct access to the system when learning a policy.
Recent work on training RL policies from offline data has shown results both
with model-free policies learned directly from the data, or with planning on
top of learnt models of the data. Model-free policies tend to be more
performant, but are more opaque, harder to command externally, and less easy to
integrate into larger systems. We propose an offline learner that generates a
model that can be used to control the system directly through planning. This
allows us to have easily controllable policies directly from data, without ever
interacting with the system. We show the performance of our algorithm,
Model-Based Offline Planning (MBOP) on a series of robotics-inspired tasks, and
demonstrate its ability leverage planning to respect environmental constraints.
We are able to find near-optimal polices for certain simulated systems from as
little as 50 seconds of real-time system interaction, and create zero-shot
goal-conditioned policies on a series of environments. An accompanying video
can be found here: https://youtu.be/nxGGHdZOFts

33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an