Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Entropy Regularization Free Mechanism for Policy-based Reinforcement
  Learning   Policy-based reinforcement learning methods suffer from the policy collapse
problem. We find valued-based reinforcement learning methods with
{\epsilon}-greedy mechanism are capable of enjoying three characteristics,
Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off,
which help value-based methods avoid the policy collapse problem. However,
there does not exist a parallel mechanism for policy-based methods that
achieves all three characteristics. In this paper, we propose an entropy
regularization free mechanism that is designed for policy-based methods, which
achieves Closed-form Diversity, Objective-invariant Exploration and Adaptive
Trade-off. Our experiments show that our mechanism is super sample-efficient
for policy-based methods and boosts a policy-based baseline to a new
State-Of-The-Art on Arcade Learning Environment.

2
The categories of this paper are: math
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ