Jan 18, 2022 · Abstract:Safety exploration can be regarded as a constrained Markov decision problem where the expected long-term cost is constrained.
Constrained reinforcement learning (CRL) enforces constraint satisfaction on the expectation of cost function while maximizing the expected return. In this ...
A novel off-policy reinforcement learning algorithm called Conservative Distributional Maximum a Posteriori Policy Optimization (CDMPO) is presented, ...
Jul 8, 2023 · Inthis paper, we present a novel off-policy reinforcement learning algorithmcalled Conservative Distributional Maximum a Posteriori Policy ...
Aug 26, 2024 · In this paper, we propose a method called Worst-Case Soft Actor Critic for safe RL that approximates the distribution of accumulated safety- ...
Nov 9, 2021 · We propose Conservative Offline Distributional Actor Critic (CODAC), an offline RL algorithm suitable for both risk-neutral and risk-averse ...
Jun 23, 2022 · SAC-Lagrangian (Ha et al., 2020) combines SAC with Lagrangian methods to address safety-constrained RL with local constraints, i.e., constraints ...
We proposeConservative Offline Distributional Actor Critic (CODAC), an offline RL algorithm suitable for both risk-neutral and risk-averse domains. CODAC adapts ...
Missing: Constraints. | Show results with:Constraints.
Jun 28, 2022 · Abstract. Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective.
Many reinforcement learning (RL) problems in practice are offline, learning purely from observational data. A key challenge is how to ensure the learned ...