To what extent do discrete token representation methods improve robustness against domain shifts in multi-task

Abstract

Abstract: This paper presents a general and systematic discussion of various symbolic representations of iterated maps through subshifts. We give a unified model for all continuous maps on a metric space, by representing a map through a general subshift over usually an uncountable alphabet. It is shown that at most the second order representation is enough for a continuous map. In particular, it is shown that the dynamics of one-dimensional continuous maps to a great extent can be transformed to the study of subshift structure of a general symbolic dynamics system. By introducing distillations, partia

Research Question

To what extent do discrete token representation methods improve robustness against domain shifts in multi-task reinforcement learning compared to continuous control baselines?

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