TY - GEN
T1 - Reinforcement learning in feature space
T2 - 37th International Conference on Machine Learning, ICML 2020
AU - Yang, Lin F.
AU - Wang, Mengdi
N1 - Publisher Copyright:
Copyright 2020 by the author(s).
PY - 2020
Y1 - 2020
N2 - Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the state-action space is large. A common practice is to parameterize the high-dimensional value and policy functions using given features. However existing methods either have no theoretical guarantee or suffer a regret that is exponential in the planning horizon H. In this paper, we propose an online RL algorithm, namely the MatrixRL, that leverages ideas from linear bandit to learn a low-dimensional representation of the probability transition model while carefully balancing the exploitation-exploration tradeoff. We show that MatrixRL achieves a regret bound O(H2dlog T √T) where d is the number of features, independent with the number of state-action pairs. MatrixRL has an equivalent kernelized version, which is able to work with an arbitrary kernel Hilbert space without using explicit features. In this case, the kernelized MatrixRL satisfies a regret bound O(H2delog T √T), where de is the effective dimension of the kernel space.
AB - Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the state-action space is large. A common practice is to parameterize the high-dimensional value and policy functions using given features. However existing methods either have no theoretical guarantee or suffer a regret that is exponential in the planning horizon H. In this paper, we propose an online RL algorithm, namely the MatrixRL, that leverages ideas from linear bandit to learn a low-dimensional representation of the probability transition model while carefully balancing the exploitation-exploration tradeoff. We show that MatrixRL achieves a regret bound O(H2dlog T √T) where d is the number of features, independent with the number of state-action pairs. MatrixRL has an equivalent kernelized version, which is able to work with an arbitrary kernel Hilbert space without using explicit features. In this case, the kernelized MatrixRL satisfies a regret bound O(H2delog T √T), where de is the effective dimension of the kernel space.
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M3 - Conference contribution
AN - SCOPUS:85105391101
T3 - 37th International Conference on Machine Learning, ICML 2020
SP - 10677
EP - 10687
BT - 37th International Conference on Machine Learning, ICML 2020
A2 - Daume, Hal
A2 - Singh, Aarti
PB - International Machine Learning Society (IMLS)
Y2 - 13 July 2020 through 18 July 2020
ER -