Search Results for author:
Found 22 papers, 7 papers with code
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.
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
+2
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.
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.
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.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels.
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Our analysis shows that UAIL outperforms existing data aggregation algorithms on a series of benchmark tasks.
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Navigating urban environments represents a complex task for automated vehicles.
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
We propose a generic approach to enforce probabilistic guarantees on an RL agent.
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.
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.
Collaborative Planning for Mixed-Autonomy Lane Merging
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.
Decomposition Methods with Deep Corrections for Reinforcement Learning
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
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).
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.
