Adaptive Primal-Dual Method for Safe Reinforcement Learning

no code implementations • 1 Feb 2024 • Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra, Santiago Paternain

In this paper, we propose, analyze and evaluate adaptive primal-dual (APD) methods for SRL, where two adaptive LRs are adjusted to the Lagrangian multipliers so as to optimize the policy in each iteration.

reinforcement-learning Safe Reinforcement Learning

Probabilistic Constraint for Safety-Critical Reinforcement Learning

no code implementations • 29 Jun 2023 • Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain

Furthermore, we propose a Safe Primal-Dual algorithm that can leverage both SPGs to learn safe policies.

reinforcement-learning Reinforcement Learning (RL)

Open Problems and Modern Solutions for Deep Reinforcement Learning

no code implementations • 5 Feb 2023 • Weiqin Chen

Deep Reinforcement Learning (DRL) has achieved great success in solving complicated decision-making problems.

Decision Making reinforcement-learning +1

Policy Gradients for Probabilistic Constrained Reinforcement Learning

no code implementations • 2 Oct 2022 • Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain

In particular, we consider the notion of probabilistic safety.

reinforcement-learning Reinforcement Learning (RL)