Abstract: The present disclosure discloses a gradient single-crystal positive electrode material, which has a chemical formula of LiNixCoyA1-x-yO2@mLiaZbOc, wherein 0<x<1, 0<y<1, 0<x+y<1, 0<m<0.05, 0.3<a?10, 1?b<4, and 1?c<15, A is at least one of Mn, Zr, Sr, Ba, W, Ti, Al, Mg, Y, and Nb, and Z is at least one of B, Al, Co, W, Ti, Zr, and Si. The atomic ratio of the content of Co on the surface of the single-crystal positive electrode material particle to the content of Ni+Co+A on the surface is greater than 0.4 and less than 0.8, and the atomic ratio of Co at a depth 10% of the radius from the surface of the single crystal positive electrode material particle is not less than 0.3; and the single-crystal positive electrode material particle has a roundness of greater than 0.4, and is free from sharp corners.

Abstract: A neural network is trained using deep reinforcement learning (DRL) techniques for adjusting cell parameters of a wireless network by generating a plurality of experience tuples, and updating the neural network based on the generated experience tuples. The trained neural network may be used to select actions to adjust the cell parameters. Each experience tuple includes a cell identifier, a first state, a second state, an action applied to the cell that moves the cell from the first state to the second state, a local reward, and a global reward. The neural network is updated based on whether or not each action is acceptable, which is determined based on the global reward and the local reward associated with each action.

Abstract: A neural network is trained using deep reinforcement learning (DRL) techniques for adjusting cell parameters of a wireless network by generating a plurality of experience tuples, and updating the neural network based on the generated experience tuples. The trained neural network may be used to select actions to adjust the cell parameters. Each experience tuple includes a cell identifier, a first state, a second state, an action applied to the cell that moves the cell from the first state to the second state, a local reward, and a global reward. The neural network is updated based on whether or not each action is acceptable, which is determined based on the global reward and the local reward associated with each action.