Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Counterfactual Data Augmentation using Locally Factored Dynamics   Many dynamic processes, including common scenarios in robotic control and
reinforcement learning (RL), involve a set of interacting subprocesses. Though
the subprocesses are not independent, their interactions are often sparse, and
the dynamics at any given time step can often be decomposed into locally
independent causal mechanisms. Such local causal structures can be leveraged to
improve the sample efficiency of sequence prediction and off-policy
reinforcement learning. We formalize this by introducing local causal models
(LCMs), which are induced from a global causal model by conditioning on a
subset of the state space. We propose an approach to inferring these structures
given an object-oriented state representation, as well as a novel algorithm for
Counterfactual Data Augmentation (CoDA). CoDA uses local structures and an
experience replay to generate counterfactual experiences that are causally
valid in the global model. We find that CoDA significantly improves the
performance of RL agents in locally factored tasks, including the
batch-constrained and goal-conditioned settings.

30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess