Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An FPGA-Based On-Device Reinforcement Learning Approach using Online
  Sequential Learning   DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement
learning using deep neural networks. DQNs require a large buffer and batch
processing for an experience replay and rely on a backpropagation based
iterative optimization, making them difficult to be implemented on
resource-limited edge devices. In this paper, we propose a lightweight
on-device reinforcement learning approach for low-cost FPGA devices. It
exploits a recently proposed neural-network based on-device learning approach
that does not rely on the backpropagation method but uses OS-ELM (Online
Sequential Extreme Learning Machine) based training algorithm. In addition, we
propose a combination of L2 regularization and spectral normalization for the
on-device reinforcement learning so that output values of the neural network
can be fit into a certain range and the reinforcement learning becomes stable.
The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a
low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate
that the proposed algorithm and its FPGA implementation complete a CartPole-v0
task 29.77x and 89.40x faster than a conventional DQN-based approach when the
number of hidden-layer nodes is 64.

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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc