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Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence
Decision and control are two of the core functionalities of high-level a...
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Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
State estimation is critical to control systems, especially when the sta...
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Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Merging into the highway from the on-ramp is an essential scenario for a...
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Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Model information can be used to predict future trajectories, so it has ...
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Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predic...
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Mixed Policy Gradient
Reinforcement learning (RL) has great potential in sequential decision-m...
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Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Safety is essential for reinforcement learning (RL) applied in real-worl...
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Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-leve...
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Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety constraints are essential for reinforcement learning (RL) applied...
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Ternary Policy Iteration Algorithm for Nonlinear Robust Control
The uncertainties in plant dynamics remain a challenge for nonlinear con...
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Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Reinforcement learning (RL) is attracting increasing interests in autono...
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Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Reinforcement learning (RL) methods often rely on massive exploration da...
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Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Reinforcement learning (RL) has achieved remarkable performance in a var...
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Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Unlike popular modularized framework, end-to-end autonomous driving seek...
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Addressing Value Estimation Errors in Reinforcement Learning with a State-Action Return Distribution Function
In current reinforcement learning (RL) methods, function approximation e...
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Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to...
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Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization
Connected vehicles will change the modes of future transportation manage...
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Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
This paper presents a constrained deep adaptive dynamic programming (CDA...
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Generalized Policy Iteration for Optimal Control in Continuous Time
This paper proposes the Deep Generalized Policy Iteration (DGPI) algorit...
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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 ...
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