no code implementations • 24 Jan 2019 • Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho, Min-Fong Hong, Chun-Yi Lee
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
no code implementations • 16 Jul 2020 • Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
1 code implementation • 24 May 2019 • Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.