In order to derive the optimal control policy, an actor-critic structure is constructed and the time-varying least square method is adopted for parameter adaptation. The derived control policy robustly stabilizes the time-varying system and guarantees an optimal control performance. As no particular ...
Then, based on the augmented system, a data-driven PI, which introduces discount factor to solve the OTCP, is implemented on an actor–critic neural network (NN) structure by only using system data rather than the exact knowledge of system dynamics. Two NNs are used in the structure to ...
H<inf>∞</inf> Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement Learning 2015, IEEE Transactions on Neural Networks and Learning Systems View all citing articles on ScopusYu Jiang was born in Xi’an, China in 1984. He received his B.S. degree in mat...
" A revolutionary movement does not spread by contamination / But by resonance / Something that constitutes itself here / Resonates with the shock wave given off by something that constituted itself elsewhere / The body that resonates does so in its ow
DDPG is a model-free, off-policy algorithm, meaning that the training phase takes place entirely before deployment and testing. A DDPG agent is also an actor–critic agent, as opposed to a value-based or a policy-based agent. In essence, this means that two different types of function app...
to Therefore, to avoid divergence off-policy DRL training algorithms maintain a copy of the actor and critic neural networks while undergoing training. DDPG usually faces convergence issues which are handled by employing various optimization algorithms among which Adam optimizer outperforms others because...
Multi-agent actor–critic for mixed cooperative-competitive environments. arXiv 2017, arXiv:1706.02275. [Google Scholar] [CrossRef] Liu, Y.; Geng, Z. Finite-time optimal formation control of multi-agent systems on the Lie group SE(3). Int. J. Control 2013, 86, 1675–1686. [Google ...
The control policy would be optimized during this process. Since the action space and the state space are continuous, from a perspective of deep RL, the actor-critic-based algorithms are well-suited. Commonly, the actor-critic structure contains a pair of neural networks (NNs) with different ...