Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Reinforcement Learning --- a New Continuous Reinforcement
  Learning Frame Based on Stochastic Differential Equation methods   Continuous reinforcement learning such as DDPG and A3C are widely used in
robot control and autonomous driving. However, both methods have theoretical
weaknesses. While DDPG cannot control noises in the control process, A3C does
not satisfy the continuity conditions under the Gaussian policy. To address
these concerns, we propose a new continues reinforcement learning method based
on stochastic differential equations and we call it Incremental Reinforcement
Learning (IRL). This method not only guarantees the continuity of actions
within any time interval, but controls the variance of actions in the training
process. In addition, our method does not assume Markov control in agents'
action control and allows agents to predict scene changes for action selection.
With our method, agents no longer passively adapt to the environment. Instead,
they positively interact with the environment for maximum rewards.

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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
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The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex