Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving
This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions. We propose a safety system that consists of two safety components: a heuristic safety and a learning-based safety. The heuristic safety module is based on common driving rules. On the other hand, the learning-based safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, it utilizes mixture density recurrent neural networks (MD-RNN) for multimodal future trajectory predictions to accelerate the learning progress. Our simulation results demonstrate that the proposed safety system outperforms previously reported results in terms of average reward and number of collisions.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement Learning (RL)Safe Reinforcement LearningSimilar Papers 제목 키워드 기반
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
Imitation learning has been widely used to learn control policies for autonomous driving based on pre-recorded data. However, imitation learning based policies have been shown to be susceptible to compounding errors when…
Autonomous DrivingImitation LearningRatio Covers of Convex Sets and Optimal Mixture Density Estimation
We study density estimation in Kullback-Leibler divergence: given an i.i.d. sample from an unknown density $p^\star$, the goal is to construct an estimator $\widehat{p}$ such that $\mathrm{KL}(p^\star,\widehat{p})$ is sm…
Density EstimationDensity Constrained Reinforcement Learning
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works. State density has a cle…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Consistent Density Estimation Under Discrete Mixture Models
This work considers a problem of estimating a mixing probability density $f$ in the setting of discrete mixture models. The paper consists of three parts. The first part focuses on the construction of an $L_1$ consistent…
Density EstimationUncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human b…
Autonomous Driving