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Computer Engineering and Applications Journal

Cooperative multi-agent reinforcement learning based on quantum computing

Xiangping Meng

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

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.

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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.
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,ant algorithm and Q-learning.First,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 probe the action,speed up learn-ing.Second,according to ant algorithm,footmark thought is presented so that Agents can be indirectly enforced to communicate with others.At last,the theory analysis and result of experiment both demonstrate that the improved Q-learning is feasible and increases the learning efficiency.
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.
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.
Reinforcement learning is a machine learning paradigm where an agent learns how to optimize its behavior solely through its interaction with the environment. It has been extensively studied and successfully applied to complex problems of many different domains in the past decades, i.e., robotics, games, scheduling. However, the performance of these algorithms becomes limited as the complexity and dimension of the state-action space increases. Recent advances in quantum computing and quantum information have sparked interest in possible applications to machine learning. By taking advantage of quantum mechanics, it is possible to efficiently process immense quantities of information and improve computational speed. In this work, we combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. From the results, we concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space.
This paper proposes a novel action selection method based on quantum computation and reinforcement learning (RL). Inspired by the advantages of quantum computation, the state/action in a RL system is represented with quantum superposition state. The probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. And the action selection is carried out by observing quantum state according to collapse postulate of quantum measurement. The results of simulated experiments show that quantum computation can be effectively used to action selection and decision making through speeding up learning. This method also makes a good tradeoff between exploration and exploitation for RL using probability characteristics of quantum theory.
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.