A Real-Time Actor-Critic Architecture for Continuous Control Reinforcement learning achieved impressive results in various challenging artificial environments and demonstrated its practical potential. In a real-world... Z Jiao,J Oh 被引量: 0发表: 2020年 The True Online Continuous Learning Automation (...
an actor-critic architecture with separate policy and value function networks policy iteration, which alternates between policy evaluation—computing the value function for a policy—and policy improvement—using the value function to obtain a better policy ...
Comparative experiments were conducted on a 2-stage OTA and a 3-stage TIA, showing that SAC outperforms DDPG and TD3 in terms of success rate, average FoM, and minimum power consumption. The results demonstrate the effectiveness of the proposed SAC-based RL architecture for analog circuit ...
5.Architecture Design Low Rank Approximation(低秩近似) 下图是低秩近似的简单示意图,左边是一个普通的全连接层,可以看到权重矩阵大小为 M*N ,而低秩近似的原理就是在两个全连接层之间再插入一层K。是不是很反直观?插入一层后,参数还能变少**?** 没错,的确变少了,我们可以看看新插入一层后的参数数量为: ...
UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecturePublished: 29 February 2024 Volume 54, pages 3529–3546, (2024) Cite this article Applied Intelligence Aims and scope Submit manuscript
The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the...
The implementation process of DDPG is the same as that of SAC (30−44), which will not be repeated here. The implementation of the OBCA-based optimization of the path planned by DDPG include the construction of objective functions and the addition of safe constraints. The basic of ...
As we all known, there are various tricks in empirical RL algorithm implementations in support the performance in practice, including hyper-parameters, normalization, network architecture or even hidden activation function, etc. I summarize some I met with the programs in this repo here: Environment...
Figure 3. Actor-dueling-critic (ADC) networks architecture. It is based on actor-critic architecture. The actor network selects actions based on the policy-gradient method; The dueling-critic network applies dueling architecture to estimate state-action values. The ADC network has better Q-value ...
environment in the form of tables, which cannot be fully modeled the complex relationship between the environment and action (Mousavi et al., 2016), and a few researchers introduced the deep neural networks into the basic RL model architecture, which is called deep reinforcement learning (DRL)....