weakness of this paper, I am concerned with the following two points: 1) The assumption for termination states of instructions are quite strong. In the general case, it is very expensive to label a large number of data manually. 2) It seems that performance and sample efficiency are sensitive to λ parameters.
(Page 9, lines 310-313) I don't understand how the process of calculating the λ
is done. How is λ
computed from step here?
(Page 8 lines 281-285) The authors explain why ELLA does not increase sample efficiency in a COMBO environment, but I don't quite understand what it means.
[1] Yuri Burda et al, Exploration by Random Network Distillation, ICLR 2019
[2] Deepak Pathak et al, Curiosity-driven Exploration by Self-supervised Prediction, ICML 2017
[3] Roberta Raileanu et al, RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments, ICLR 2020
1) The assumption for termination states of instructions are quite strong. In the general case, it is very expensive to label a large number of data manually.