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...
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...
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). 展开 会议名称: International Conference on Computing Systems and ...
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
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...
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
Subsequently, based on the evaluation results,a deep deterministic policy gradient (DDPG) algorithm relying on prioritized experience replay (PER) is used toformulate a real-time electricity price plan. Ultimately, V...
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....
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....