… input constraints, a reinforcement learning-based finite-time … -critic reinforcement learning
(RL) mechanism with finite-time control … which requires infinite-time convergence thereby rather …

… Hence, there exist constrained reinforcement learning … to solve reinforcement learning
problems with constraints. Thus, as we … 1 that this time average should satisfy the constraints with …

… In standard reinforcement learning (RL), a learning agent … are more naturally expressed
as constraints. For instance, the … of constraints in RL tasks: specifically, any constraints that …

… Mowbray et al., 2022) yield policies that arbitrarily violate an anytime constraint. This …
blowup for anytime constraints is in the time horizon, we focus on varying the time horizon. …

… Reinforcement Learning frameworks solving HJB equations … free Integral Reinforcement
Learning (IRL) framework to learn the … policy u ϵ ∗ ( x ) where the control is subject to constraint. …

… as completing tasks using the least amount of time/energy, learning multiple tasks, or dealing
with multiple opponents. In the context of reinforcement learning (RL), these problems are …

… The constrained-input coupled with the inability to identify … of stabilizing controller based
on reinforcement-learning (RL) … -time uncertain nonlinear systems subject to input constraints. …

… is only for service level setting, and during the selection of component service we take the
constraints of composite service as a whole. Here, taking the response time constraint as an …

… , and formalize it as a constrained MDP with two criteria. The … We present a model free,
heuristic reinforcement learning … a feasible solution for the constrained problem that has a good …

… reinforcement learning is Q-learning [10]. To favor the exploration, we initialize the learning
… The computation time for simulating deployment and reinforcement learning are negligible. …