Search Results for author: Kikuo Fujimura

Found 20 papers, 6 papers with code

Robust Driving Policy Learning with Guided Meta Reinforcement Learning

no code implementations19 Jul 2023 Kanghoon Lee, Jiachen Li, David Isele, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer

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.

Autonomous Navigation Meta Reinforcement Learning +1

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

no code implementations6 Mar 2022 Xiaobai Ma, David Isele, Jayesh K. Gupta, Kikuo Fujimura, Mykel J. Kochenderfer

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

Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic

no code implementations25 May 2020 Maxime Bouton, Alireza Nakhaei, David Isele, Kikuo Fujimura, Mykel J. Kochenderfer

This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies.

Autonomous Vehicles reinforcement-learning +1

Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals

no code implementations27 Sep 2019 Anahita Mohseni-Kabir, David Isele, Kikuo Fujimura

We investigate the problem of multi-agent reinforcement learning, focusing on decentralized learning in non-stationary domains for mobile robot navigation.

Autonomous Driving Multi-agent Reinforcement Learning +3

Safe Reinforcement Learning on Autonomous Vehicles

no code implementations27 Sep 2019 David Isele, Alireza Nakhaei, Kikuo Fujimura

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.

Autonomous Vehicles reinforcement-learning +2

Driving in Dense Traffic with Model-Free Reinforcement Learning

2 code implementations15 Sep 2019 Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei, Kikuo Fujimura, Maxim Likhachev

Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.

Continuous Control reinforcement-learning +1

Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network

1 code implementation9 Sep 2019 Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura

This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.

Autonomous Driving

Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

1 code implementation26 Jun 2019 Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer

In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels.

Autonomous Vehicles Decision Making +3

Uncertainty-Aware Data Aggregation for Deep Imitation Learning

no code implementations7 May 2019 Yuchen Cui, David Isele, Scott Niekum, Kikuo Fujimura

Our analysis shows that UAIL outperforms existing data aggregation algorithms on a series of benchmark tasks.

Autonomous Driving Imitation Learning

Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios

no code implementations12 Apr 2019 Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka, Kikuo Fujimura

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.

Autonomous Vehicles Common Sense Reasoning +2

CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

1 code implementation ICLR 2020 Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, Hongyuan Zha

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.

Autonomous Vehicles Efficient Exploration +3

Collaborative Planning for Mixed-Autonomy Lane Merging

no code implementations7 Aug 2018 Shray Bansal, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.

Decision Making

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations

no code implementations1 Jun 2018 Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura

This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.

Decision Making

Decomposition Methods with Deep Corrections for Reinforcement Learning

1 code implementation6 Feb 2018 Maxime Bouton, Kyle Julian, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer

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.

Autonomous Driving Decision Making +5

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

no code implementations2 May 2017 David Isele, Reza Rahimi, Akansel Cosgun, Kaushik Subramanian, Kikuo Fujimura

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers.

Autonomous Vehicles Navigate +2

Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections

no code implementations2 May 2017 David Isele, Akansel Cosgun, Kikuo Fujimura

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).

Transfer Learning

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