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06/21/2023
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
Large-scale driving datasets such as Waymo Open Dataset and nuScenes sub...
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03/03/2023
Guarded Policy Optimization with Imperfect Online Demonstrations
The Teacher-Student Framework (TSF) is a reinforcement learning setting ...
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10/12/2022
TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
Diverse and realistic traffic scenarios are crucial for evaluating the A...
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05/31/2022
Human-AI Shared Control via Frequency-based Policy Dissection
Human-AI shared control allows human to interact and collaborate with AI...
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04/05/2022
Action-Conditioned Contrastive Policy Pretraining
Deep visuomotor policy learning achieves promising results in control ta...
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03/19/2022
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
End-to-end scene text spotting has attracted great attention in recent y...
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02/17/2022
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Human intervention is an effective way to inject human knowledge into th...
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10/26/2021
Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization
Self-Driven Particles (SDP) describe a category of multi-agent systems c...
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10/13/2021
Safe Driving via Expert Guided Policy Optimization
When learning common skills like driving, beginners usually have domain ...
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09/26/2021
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Driving safely requires multiple capabilities from human and intelligent...
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07/09/2021
Safe Exploration by Solving Early Terminated MDP
Safe exploration is crucial for the real-world application of reinforcem...
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12/26/2020
Improving the Generalization of End-to-End Driving through Procedural Generation
Recently there is a growing interest in the end-to-end training of auton...
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06/14/2020
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
Conventional Reinforcement Learning (RL) algorithms usually have one sin...
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05/21/2020
Novel Policy Seeking with Constrained Optimization
In this work, we address the problem of learning to seek novel policies ...
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07/27/2018
AXNet: ApproXimate computing using an end-to-end trainable neural network
Neural network based approximate computing is a universal architecture p...
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05/23/2018
