In this approach, an actor-critic scheme is employed to improve the policy for a given Lagrange parameter update on a faster timescale as in the classical actor-critic architecture. A meta actor-critic scheme using this faster timescale policy updates is then employed to improve the Lagrange ...
We develop an online actor–critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable policy...
Actor–critic learning based PID control for robotic manipulators 2024, Applied Soft Computing Show abstract Adaptive coordinated control for space manipulators with input saturation 2023, Journal of the Franklin Institute Show abstract Fuzzy H<inf>∞</inf> robust control for T-S aero-engine systems ...
Vamvoudakis, K.G., Lewis, F.L.: Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica 46(5), 878–888 (2010) MathSciNet MATH Google Scholar Lamperski, A., Ames, D.: Lyapunov theory for Zeno stability. IEEE Trans. Autom. Contro...
(2020) employed the Sharpe ratio to automatically select the best-performing agent from an ensemble of proximal policy optimization (PPO), advantage actor–critic (A2C), and deep deterministic policy gradient (DDPG) algorithms. In this method, the three deep reinforcement learning (DRL) experts ...
Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem ...
Variance-constrained actor-critic algorithms for discounted and average reward MDPs. Machine Learning, 105(3):367-417, dec 2016.LA Prashanth and Mohammad Ghavamzadeh. Variance-constrained actor-critic algorithms for dis- counted and average reward mdps. Machine Learning, 105(3):367-417, 2016....
To this end, we utilize the Lagrangian formulation and propose actor-critic algorithms. Through experiments on a constrained multi-agent grid world task, we demonstrate that our algorithms converge to near-optimal joint action sequences satisfying the given constraints.Raghuram Bharadwaj Diddigi...
V. S. Borkar, "An actor-critic algorithm for constrained Markov decision processes," Systems and Control Letters, vol. 54, no. 3, pp. 207-213, 2005.V. Borkar, "An actor-critic algorithm for constrained Markov decision processes", Systems & Control Letters, 54:207-213, 2005....
Constrained optimizationWe propose a novel actor–critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent direction that is motivated by the solution of a certain non-linear optimization problem. We also discuss ...