A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
Last-iterate convergent policy gradient primal-dual methods for constrained mdps
We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …
constrained Markov decision process (constrained MDP). Despite the popularity of …
Last-iterate global convergence of policy gradients for constrained reinforcement learning
Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems
where agents are required to achieve goals by maximizing the expected return while …
where agents are required to achieve goals by maximizing the expected return while …
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
Gradient Descent Ascent (GDA) methods for min-max optimization problems typically
produce oscillatory behavior that can lead to instability, eg, in bilinear settings. To address …
produce oscillatory behavior that can lead to instability, eg, in bilinear settings. To address …