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 ...
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...
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....
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...
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...
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
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