Extinction probabilities for the homogeneous birth and death chain in Markovian environment with an absorption barrier 0 and a reflection barrier m.
求出了0为吸收壁，M为反射壁的马氏环境中齐次生灭链的灭绝概率。
Due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to multi-agent system.
由于强化学习理论的限制，在多智能体系统中马尔科夫过程模型不再适用，因此不能把强化学习直接用于多智能体的协作学习问题。
In addition, as the memory effect of the environment, entanglement can partially revive after it vanishes, and the revival degree is dependent on the strength of non-Markovian effect.
另一方面，由于环境的记忆作用，纠缠在退化后能够部分恢复，恢复的程度取决于环境非马尔科夫性质的强弱。
We do not make the rotating-wave and markovian approximations on the interaction Hamiltonian and treat the environment as a non-markovian reservoir to the oscillators.
本文推导了三个全同的谐振子系统与一个非马尔可夫库相互作用时满足的非马尔可夫主方程，并在此基础上讨论了系统的三模纠缠和压缩。
We do not make the rotating-wave and markovian approximations on the interaction Hamiltonian and treat the environment as a non-markovian reservoir to the oscillators.
本文推导了三个全同的谐振子系统与一个非马尔可夫库相互作用时满足的非马尔可夫主方程，并在此基础上讨论了系统的三模纠缠和压缩。
应用推荐