Search Results for author:
Found 11 papers, 8 papers with code
Efficient Duple Perturbation Robustness in Low-rank MDPs
The pursuit of robustness has recently been a popular topic in reinforcement learning (RL) research, yet the existing methods generally suffer from efficiency issues that obstruct their real-world implementation.
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
Moreover, to handle the sim-to-real gap in the dynamics, we propose a skill discovery algorithm that learns new skills caused by the sim-to-real gap from real-world data.
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
This paper presents an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear stochastic systems.
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function.
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy.
Model-based Reinforcement Learning
reinforcement-learning
+2
Reachability Constrained Reinforcement Learning
Recent studies incorporate feasible sets into CRL with energy-based methods such as control barrier function (CBF), safety index (SI), and leverage prior conservative estimations of feasible sets, which harms the performance of the learned policy.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.
