Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binarized P-Network: Deep Reinforcement Learning of Robot Control from
  Raw Images on FPGA   This paper explores a Deep Reinforcement Learning (DRL) approach for
designing image-based control for edge robots to be implemented on Field
Programmable Gate Arrays (FPGAs). Although FPGAs are more power-efficient than
CPUs and GPUs, a typical DRL method cannot be applied since they are composed
of many Logic Blocks (LBs) for high-speed logical operations but low-speed
real-number operations. To cope with this problem, we propose a novel DRL
algorithm called Binarized P-Network (BPN), which learns image-input control
policies using Binarized Convolutional Neural Networks (BCNNs). To alleviate
the instability of reinforcement learning caused by a BCNN with low function
approximation accuracy, our BPN adopts a robust value update scheme called
Conservative Value Iteration, which is tolerant of function approximation
errors. We confirmed the BPN's effectiveness through applications to a visual
tracking task in simulation and real-robot experiments with FPGA.

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The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
18
The categories of this paper are: econ
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas