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Found 20 papers, 4 papers with code
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation
Real-world driving involves intricate interactions among vehicles navigating through dense traffic scenarios.
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
This paper presents an online smooth-path lane-change control framework.
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents.
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment.
Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach
To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a "shielding" scheme), which overrides the robot's dual control policy with a safety fallback strategy when a safety-critical event is imminent.
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants.
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
This paper proposes a discretionary lane selection algorithm.
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios.
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies.
Interactive Decision Making for Autonomous Vehicles in Dense Traffic
Dense urban traffic environments can produce situations where accurate prediction and dynamic models are insufficient for successful autonomous vehicle motion planning.
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals
We investigate the problem of multi-agent reinforcement learning, focusing on decentralized learning in non-stationary domains for mobile robot navigation.
Safe Reinforcement Learning on Autonomous Vehicles
There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications.
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Our analysis shows that UAIL outperforms existing data aggregation algorithms on a series of benchmark tasks.
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
Selective Experience Replay for Lifelong Learning
Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence.
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections
We view intersection handling on autonomous vehicles as a reinforcement learning problem, and study its behavior in a transfer learning setting.
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer
Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer.
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers.
Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections
We analyze how the knowledge to autonomously handle one type of intersection, represented as a Deep Q-Network, translates to other types of intersections (tasks).
