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Quantum Tree-Based Planning
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DOI：10.1109/ACCESS.2021.3110652.png)
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Abstract
En 中文
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.
Keywords:
Planning
Heuristic algorithms
Quantum computing
Reinforcement learning
Qubit
Encoding
Quantum algorithm
Quantum computation
quantum reinforcement learning
sparse sampling
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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