We used the Deep Deterministic Policy Gradient (DDPG) variant, which adapts to continuous data and improves the secrecy rate by considering, in the algorithm, the best sample obtained via a Prioritized Experiment Replay (PER).Lammari, Amina...
切换阈值等动态信息构建越区切换模型.同时针对算法时间成本复杂度及稳定性,采用优先经验回放深度确定性策略梯度(Prioritized Experience Replay-Deep Deterministic Policy Gradient,PER-DDPG)算法,将列车状态空间信息传输至PER-DDPG网络中进行优化分析.结果表明基于PER-DDPG算法优化后的列车越区切换模型使用该算法时间计算成本...
该文设计了仿真场景进行模型的训练和测试,并对深度确定性策略梯度(DDPG), 结合优先经验回放机制的深度确定性策略梯度(PER-DDPG),结合优先经验回放机制和课程学习机制 的深度确定性策略梯度(CLPER-DDPG)3种算法进行对比实验,并在园区内的真实道路上进行实车实 验.结果表明:相比于DDPG算法,CLPER-DDPG算法使规划器的...
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL) - XinJingHao/DRL-Pytorch
切换阈值等动态信息构建越区切换模型.同时针对算法时间成本复杂度及稳定性,采用优先经验回放深度确定性策略梯度(Prioritized Experience Replay-Deep Deterministic Policy Gradient,PER-DDPG)算法,将列车状态空间信息传输至PER-DDPG网络中进行优化分析.结果表明基于PER-DDPG算法优化后的列车越区切换模型使用该算法时间计算成本...
基于多层感知机设计了车辆纵向速度规划器,构建了结合优先经验回放机制和课程学习机制的深度确定性策略梯度算法.该文设计了仿真场景进行模型的训练和测试,并对深度确定性策略梯度(DDPG),结合优先经验回放机制的深度确定性策略梯度(PER-DDPG),结合优先经验回放机制和课程学习机制的深度确定性策略梯度(CLPER-DDPG)3种算法...
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL) - collapse-del/DRL-Pytorch
同时针对算法时间成本复杂度及稳定性,采用优先经验回放深度确定性策略梯度(Prioritized Experience Replay-Deep Deterministic Policy Gradient,PER-DDPG)算法,将列车状态空间信息传输至PER-DDPG网络中进行优化分析。结果表明基于PER-DDPG算法优化后的列车越区切换模型使用该算法时间计算成本降低,数据包传输延时约降低55%。 【...
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DDPG:Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning[J]. arXiv preprint arXiv:1509.02971, 2015. TD3:Fujimoto S, Hoof H, Meger D. Addressing function approximation error in actor-critic methods[C]//International conference on machine learning....