Abstract
Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.
Highlights
As an effective tool for Artificial Intelligence (AI) research, the simulation platform allows the agent to rely on the predefined algorithm to perform various kinds of actions in a certain scenario, which plays a role in replacing the real physical environment [2]
The scenes of Multi-Robot Confrontation are usually modeled as a semi-Markov decision-making process, and there are some strategy selection models that are based on classical reinforcement learning
The robot confrontation process is regarded as an Semi-Markov decision processes (SMDPs), and the agent may take a serial of the same actions before transiting into the state
Summary
The aim of Artificial Intelligence (AI) is to develop a computer program that can realize human-level intelligence, self-consciousness, and knowledge application. As an effective tool for AI research, the simulation platform allows the agent to rely on the predefined algorithm to perform various kinds of actions in a certain scenario, which plays a role in replacing the real physical environment [2]. These simulations can be used as substitutes for the physical environment that can touch but can be set to some scenarios that cannot exist in real-life depending on our imagination [3]. This year, it has attracted extensive attention from scholars [10,11,12]
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