Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

The heuristic dynamic programming (HDP)()-based optimal control strategy, which takes a
long-term prediction parameter into account using an iterative manner, accelerates the …

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for
large-scale continuous controls. DDPG runs the back-propagation from the state-action …

An optimal robust control solution for general nonlinear systems with unknown but
observable dynamics is advanced here. The underlying Hamilton-Jacobi-Isaacs (HJI) …

Because of a powerful temporal-difference (TD) with λ [TD (λ)] learning method, this paper
presents a novel n-step adaptive dynamic programming (ADP) architecture that combines …

In problems with complex dynamics and challenging state spaces, the dual heuristic
programming (DHP) algorithm has been shown theoretically and experimentally to perform …

In this article, the-policy iteration (-PI) method for the optimal control problem of discrete-time
linear systems is reconsidered and restated from a novel aspect. First, the traditional-PI …

Wastewater treatment is important for maintaining a balanced urban ecosystem. To ensure
the success of wastewater treatment, the tracking error between the crucial variable …

In this article, the adaptive neural control algorithm based on the λ-return mechanism [ANC
(λ)] is developed under the temporal difference learning framework to address the optimal …

As an effective optimal control scheme in the field of reinforcement learning, adaptive
dynamic programming (ADP) has attracted extensive attention in recent decades. Neural …