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Found 17 papers, 5 papers with code
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.
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.
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model
Formally, MPG is constructed as a weighted average of the data-driven and model-driven PGs, where the former is the derivative of the learned Q-value function, and the latter is that of the model-predictive return.
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
CTM: Collaborative Temporal Modeling for Action Recognition
In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure 1) to learn temporal information for action recognition.
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.
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.
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety is essential for reinforcement learning (RL) applied in real-world situations.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
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.
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).
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.
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 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.
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
Self-evolution is indispensable to realize full autonomous driving.
