Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning with Deep Energy-Based Policies   We propose a method for learning expressive energy-based policies for
continuous states and actions, which has been feasible only in tabular domains
before. We apply our method to learning maximum entropy policies, resulting
into a new algorithm, called soft Q-learning, that expresses the optimal policy
via a Boltzmann distribution. We use the recently proposed amortized Stein
variational gradient descent to learn a stochastic sampling network that
approximates samples from this distribution. The benefits of the proposed
algorithm include improved exploration and compositionality that allows
transferring skills between tasks, which we confirm in simulated experiments
with swimming and walking robots. We also draw a connection to actor-critic
methods, which can be viewed performing approximate inference on the
corresponding energy-based model.

30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio