Along with the increasing need for rescue robots in disasters such as earthquakes and tsunami, there is an urgent need to develop robotics software for learning and adapting to any environment. A reinforcement learnin...Along with the increasing need for rescue robots in disasters such as earthquakes and tsunami, there is an urgent need to develop robotics software for learning and adapting to any environment. A reinforcement learning (RL) system that improves agents’ policies for dynamic environments by using a mixture model of Bayesian networks has been proposed, and is effective in quickly adapting to a changing environment. However, the increase in computational complexity requires the use of a high-performance computer for simulated experiments and in the case of limited calculation resources, it becomes necessary to control the computational complexity. In this study, we used an RL profit-sharing method for the agent to learn its policy, and introduced a mixture probability into the RL system to recognize changes in the environment and appropriately improve the agent’s policy to adjust to a changing environment. We also introduced a clustering distribution that enables a smaller, suitable selection, while maintaining a variety of mixture probability elements in order to reduce the computational complexity and simultaneously maintain the system’s performance. Using our proposed system, the agent successfully learned the policy and efficiently adjusted to the changing environment. Finally, control of the computational complexity was effective, and the decline in effectiveness of the policy improvement was controlled by using our proposed system.展开更多
There are many proposed policy-improving systems of Reinforcement Learning (RL) agents which are effective in quickly adapting to environmental change by using many statistical methods, such as mixture model of Bayesi...There are many proposed policy-improving systems of Reinforcement Learning (RL) agents which are effective in quickly adapting to environmental change by using many statistical methods, such as mixture model of Bayesian Networks, Mixture Probability and Clustering Distribution, etc. However such methods give rise to the increase of the computational complexity. For another method, the adaptation performance to more complex environments such as multi-layer environments is required. In this study, we used profit-sharing method for the agent to learn its policy, and added a mixture probability into the RL system to recognize changes in the environment and appropriately improve the agent’s policy to adjust to the changing environment. We also introduced a clustering that enables a smaller, suitable selection in order to reduce the computational complexity and simultaneously maintain the system’s performance. The results of experiments presented that the agent successfully learned the policy and efficiently adjusted to the changing in multi-layer environment. Finally, the computational complexity and the decline in effectiveness of the policy improvement were controlled by using our proposed system.展开更多
文摘Along with the increasing need for rescue robots in disasters such as earthquakes and tsunami, there is an urgent need to develop robotics software for learning and adapting to any environment. A reinforcement learning (RL) system that improves agents’ policies for dynamic environments by using a mixture model of Bayesian networks has been proposed, and is effective in quickly adapting to a changing environment. However, the increase in computational complexity requires the use of a high-performance computer for simulated experiments and in the case of limited calculation resources, it becomes necessary to control the computational complexity. In this study, we used an RL profit-sharing method for the agent to learn its policy, and introduced a mixture probability into the RL system to recognize changes in the environment and appropriately improve the agent’s policy to adjust to a changing environment. We also introduced a clustering distribution that enables a smaller, suitable selection, while maintaining a variety of mixture probability elements in order to reduce the computational complexity and simultaneously maintain the system’s performance. Using our proposed system, the agent successfully learned the policy and efficiently adjusted to the changing environment. Finally, control of the computational complexity was effective, and the decline in effectiveness of the policy improvement was controlled by using our proposed system.
文摘There are many proposed policy-improving systems of Reinforcement Learning (RL) agents which are effective in quickly adapting to environmental change by using many statistical methods, such as mixture model of Bayesian Networks, Mixture Probability and Clustering Distribution, etc. However such methods give rise to the increase of the computational complexity. For another method, the adaptation performance to more complex environments such as multi-layer environments is required. In this study, we used profit-sharing method for the agent to learn its policy, and added a mixture probability into the RL system to recognize changes in the environment and appropriately improve the agent’s policy to adjust to the changing environment. We also introduced a clustering that enables a smaller, suitable selection in order to reduce the computational complexity and simultaneously maintain the system’s performance. The results of experiments presented that the agent successfully learned the policy and efficiently adjusted to the changing in multi-layer environment. Finally, the computational complexity and the decline in effectiveness of the policy improvement were controlled by using our proposed system.
