Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monte-Carlo Tree Search for Policy Optimization   Gradient-based methods are often used for policy optimization in deep
reinforcement learning, despite being vulnerable to local optima and saddle
points. Although gradient-free methods (e.g., genetic algorithms or evolution
strategies) help mitigate these issues, poor initialization and local optima
are still concerns in highly nonconvex spaces. This paper presents a method for
policy optimization based on Monte-Carlo tree search and gradient-free
optimization. Our method, called Monte-Carlo tree search for policy
optimization (MCTSPO), provides a better exploration-exploitation trade-off
through the use of the upper confidence bound heuristic. We demonstrate
improved performance on reinforcement learning tasks with deceptive or sparse
reward functions compared to popular gradient-based and deep genetic algorithm
baselines.

28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg