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Found 30 papers, 5 papers with code
Reachability Constrained Reinforcement Learning
We characterize the feasible set by the established self-consistency condition, then a safety value function can be learned and used as constraints in CRL.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
Zeroth-Order Actor-Critic
Zeroth-order optimization methods and policy gradient based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.
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.
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections
This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows.
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow
Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians.
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Based on this, the penalty method is formulated as a proportional controller, and the Lagrangian method is formulated as an integral controller.
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.
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error.
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC).
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.
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.
Mixed Policy Gradient
MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.
Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems
This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems.
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Taking a control perspective, we first interpret the penalty method and the Lagrangian method as proportional feedback and integral feedback control, respectively.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
Robust Memory Augmentation by Constrained Latent Imagination
The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way.
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety is essential for reinforcement learning (RL) applied in real-world situations.
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
The simulation results show that the TPI algorithm can converge to the optimal solution for the linear plant, and has high resistance to disturbances for the nonlinear plant.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm.
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
A sequential latent environment model is introduced and learned jointly with the reinforcement learning process.
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors
In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance.
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.
Generalized Policy Iteration for Optimal Control in Continuous Time
This paper proposes the Deep Generalized Policy Iteration (DGPI) algorithm to find the infinite horizon optimal control policy for general nonlinear continuous-time systems with known dynamics.
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
Distributed Model Predictive Control for Heterogeneous V ehicle Platoons Under Unidirectional Topologies
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point.
