… Based on a complete search procedure, it will always find the optimal solution if we allow
an execution time large enough. A critical design choice, that makes CP non-trivial to use in …

… 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. …

… 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 …

… forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring
safety during the learning … Enforcing the RL agent to stay safe during learning might limit the …

… Safe reinforcement learning … constraints. Specifically, we take a stepwise approach for
optimizing safety and cumulative reward. In our method, the agent first learns safety constraints by …

… of the policy through time we index them as πi to indicate the learning iteration. An existing
… constraints on the environment in this context. Constrained MDPs define a constrained …

… time frame, which is able to aptly map states and times to … deterministic constraints that need
to be satisfied over multiple time … the joint context of constrained Partially Observable Markov …

… and time constraints are not important. In contrast, MPC seems best applied to continuous
tasks with complex, time… , and for which state and time constraints are very important. However, …

… is the one that maximizes the reward and at the same time satisfies the cost constraints. …
our algorithm that involves stochastic selection of a constraint if multiple constraints are violated. …