Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Reinforcement learning is widely used in applications where one needs to perform sequential decisions while interacting with the environment. The problem becomes more challenging when the decision requirement includes satisfying some safety constraints. The problem is mathematically formulated as constrained Markov decision process (CMDP). In the literature, various algorithms are available to solve CMDP problems in a model-free manner to achieve $\epsilon$-optimal cumulative reward with $\epsilon$ feasible policies. An $\epsilon$-feasible policy implies that it suffers from constraint violation. An important question here is whether we can achieve $\epsilon$-optimal cumulative reward with zero constraint violations or not. To achieve that, we advocate the use of randomized primal-dual approach to solve the CMDP problems and propose a conservative stochastic primal-dual algorithm (CSPDA) which is shown to exhibit $\tilde{\mathcal{O}}\left(1/\epsilon^2\right)$ sample complexity to achieve $\epsilon$-optimal cumulative reward with zero constraint violations. In the prior works, the best available sample complexity for the $\epsilon$-optimal policy with zero constraint violation is $\tilde{\mathcal{O}}\left(1/\epsilon^5\right)$. Hence, the proposed algorithm provides a significant improvement as compared to the state of the art.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision Makingreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm
We consider the problem of constrained Markov decision process (CMDP) in continuous state-actions spaces where the goal is to maximize the expected cumulative reward subject to some constraints. We propose a novel Conser…
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Safety is a critical hurdle that limits the application of deep reinforcement learning (RL) to real-world control tasks. To this end, constrained reinforcement learning leverages cost functions to improve safety in const…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning
In many RL applications, ensuring an agent's actions adhere to constraints is crucial for safety. Most previous methods in Action-Constrained Reinforcement Learning (ACRL) employ a projection layer after the policy netwo…
reinforcement-learningReinforcement LearningLearning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret …
Safe ExplorationLearn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough data and prior model about the dangerous …
reinforcement-learningReinforcement Learning (RL)