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International Conference on Natural Computation (ICNC)

Quantum Chaotic Reinforcement Learning

Xiangping Meng, Jun Meng, Li-Juan Lui

Published Jan 1, 2008• 5 citations
Publisher

Abstract

A novel learning policy in multi-agent reinforcement learning is presented, trying to find another tradeoff of exploration and exploitation efficiently, It use the output of the classical quantum computer as an input for chaotic dynamics amplifier, The novel amplifier consider the chaotic effect, it can amplify the initial value in polynomial time. It considers the action selection problem and argues that the problem, in principle, can be solved in polynomial time if it combines the quantum computer with the chaotic dynamics amplifier based on the logistic map.

DOI
10.1109/icnc.2008.99

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A novel learning policy in multi-agent reinforcement learning is presented, trying to find another tradeoff of exploration and exploitation efficiently, It use the output of the classical quantum computer as an input for chaotic dynamics amplifier, The novel amplifier consider the chaotic effect, it can amplify the initial value in polynomial time. It considers the action selection problem and argues that the problem, in principle, can be solved in polynomial time if it combines the quantum computer with the chaotic dynamics amplifier based on the logistic map.
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A novel learning policy in multiagent reinforcement learning is presented, trying to find another tradeoff of exploration and exploitation efficiently, which is different from traditional greedy or softmax action selection method. The state and action of multiagent are represented with quantum superposition state, and probability amplitude is used to denote the probability of an action. Quantum search algorithm is adopted in multiagent action selection. The experiment results show that the new algorithm is effective and can help multiagent learn faster. This combination of quantum computing with multiagent reinforcement learning is an attempt, and the idea possibly brings more researches in multiagent reinforcement learning.
Due to the interactions among the agents in the cooperative multi-agent systems,multi-agent learning problem complexity can rise rapidly with the number of agents or their behavioral sophistication.In order to converge to desirable equilibrium,agents generally require sufficient exploration of strategy space and coordinate their policies to achieve optimal equilibrium.A novel cooperative multi-agent learning method is proposed based on quantum theory and reinforcement learning.This method not only coordinates agents’ behaviors using quantum entanglement and helps agents make action selection under quantum superposition,but also adopts Grover’s searching algorithm which can speed up learning.This method also makes a good tradeoff between exploration and exploitation using probability characteristics of quantum theory.The results of simulated experiments show that quantum theory can be effectively used to cooperative multi-agent reinforcement learning.