Transfer learning through bbstraction using learning vector quantization
Reinforcement learning (RL) enables an agent to find a solution to a problem by interacting with the environment. However, the learning process always starts from scratch andpossibly takes a long time. Here, knowledge transfer betweentasks is considered. In this paper, we argue that an abstraction c...
à®®à¯à®´à¯ விளகà¯à®•à®®à¯
Saved in:
| தலைமை எழà¯à®¤à¯à®¤à®¾à®³à®°à¯à®•ளà¯: | , , |
|---|---|
| வடிவமà¯: | Conference or Workshop Item |
| வெளியீடபà¯à®ªà®Ÿà¯à®Ÿà®¤à¯: |
2017
|
| பகà¯à®¤à®¿à®•ளà¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•லà¯: | http://umpir.ump.edu.my/19465/ http://umpir.ump.edu.my/19465/1/Transfer%20Learning%20through%20Abstraction%20Using%20Learning%20Vector%20Quantization.pdf http://umpir.ump.edu.my/19465/2/Transfer%20Learning%20through%20Abstraction%20Using%20Learning%20Vector%20Quantization%201.pdf |
| கà¯à®±à®¿à®¯à¯€à®Ÿà¯à®•ளà¯: |
கà¯à®±à®¿à®šà¯à®šà¯Šà®²à¯ இணை
கà¯à®±à®¿à®¯à¯€à®Ÿà¯à®•ள௠இலà¯à®²à¯ˆ, இநà¯à®¤ கà¯à®±à®¿à®šà¯à®šà¯Šà®²à¯à®²à¯ˆ à®®à¯à®¤à®²à®¿à®²à¯ பதிவ௠செயà¯à®¯à¯à®™à¯à®•ளà¯!
|