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Clear Water Bay, Kowloon
Multi-model Approach to Non-stationary Reinforcement Learning
Samuel P. M. Choi, Dit Yan Yeung, Nevin L. Zhang
Department of Computer Science
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon
Hong Kongfpmchoi,dyyeung,lzhangg@cs.ust.hk
ABSTRACT
This paper proposes a novel alogrithm for a class of non-
stationary reinforcement learning problems in which the
environmental changes are rare and finite. Through dis-
carding corrupted models and combining similar ones, the
proposed algorithm maintains a collection of frequently
encountered environment models and enables an effective
adaptation when a similar environment recurs. The algo-
rithm has empirically compared with the finite window ap-
proach, a widely-used method for non-stationary RL prob-
lems. Results have shown that our algorithm consistently
outperforms the finite window approach in various empiri-
cal setups.
KEY WORDS
Reinforcement Learning, Non-stationary Environment
1 Introduction
Learning to perform sequential decision tasks in a com-
plex environment is non-trivial, especially when the envi-
ronment is not known in advance. Reinforcement learn-
ing (RL) [4, 9] is a computational approach to such a task
through learning from interaction. Thus far, most existing
RL researches are focussed on stationary Markovian envi-
ronments; i.e., the underlying dynamics of the environment
depend solely on the current state and are independent of
time. Non-stationary environments, on the contrary, refer
to the stochastic environments in which the underlying pa-
rameters may vary over time.
Non-stationary problems are very common in the real
world. Consider a robot rover which explores in an un-
visited planet. When roaming around, the rover may en-
counter various types of weathers and terrains (e.g. uphill,
downhill, and craters). In order to navigate in a desired
manner, the rover may need different sequence of control
actions for each environment. Hence, it would be inef-
fective to treat all
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