Name
Abstract | ||
|---|---|---|
Q-learning is an off-policy reinforcement learning technique, which has the main advantage of obtaining an optimal policy interacting with an unknown model environment. This paper proposes a parallel fixed-point Q-learning algorithm architecture implemented on field programmable gate arrays (FPGA) focusing on optimizing the system processing time. The convergence results are presented, and the processing time and occupied area were analyzed for different states and actions sizes scenarios and various fixed-point formats. The studies concerning the accuracy of the Q-learning technique response and resolution error associated with a decrease in the number of bits were also carried out for hardware implementation. The architecture implementation details were featured. The entire project was developed using the system generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2885950 | IEEE ACCESS |
Keywords | Field | DocType |
FPGA,Q-learning,reinforcement learning,reconfigurable computing | Computer architecture,Computer science,Q-learning,Field-programmable gate array,Reinforcement learning,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucileide M. D. Da Silva | 1 | 0 | 0.34 |
| Matheus F. Torquato | 2 | 2 | 2.74 |
| M. A.C. Fernandes | 3 | 15 | 8.23 |