Safe reinforcement learning is extremely challenging--not only must the agent explore an
unknown environment, it must do so while ensuring no safety constraint violations. We …

Meta-reinforcement learning has widely been used as a learning-to-learn framework to
solve unseen tasks with limited experience. However, the aspect of constraint violations has …

Existing studies on constrained reinforcement learning (RL) may obtain a well-performing
policy in the training environment. However, when deployed in a real environment, it may …

Constrained reinforcement learning is to maximize the expected reward subject to
constraints on utilities/costs. However, the training environment may not be the same as the …

Abstract We study convex Constrained Markov Decision Processes (CMDPs) in which the
objective is concave and the constraints are convex in the state-action occupancy measure …

We examine online safe multi-agent reinforcement learning using constrained Markov
games in which agents compete by maximizing their expected total rewards under a …

The problem of constrained Markov decision process is considered. An agent aims to
maximize the expected accumulated discounted reward subject to multiple constraints on its …

Reinforcement learning (RL) is widely used in applications where one needs to perform
sequential decision-making while interacting with the environment. The standard RL …

We study constrained sequential decision-making problems modeled by constrained
Markov decision processes with potentially infinite state spaces. We propose a Bregman …

В данной статье проведен обзор как исторических достижений, так и современных
результатов в области марковских процессов принятия решений (Markov Decision …