Publications

A list of publications that have used HighwayEnv. If your publication uses HighwayEnv and you wish for it to be included, please open a pull request.

Papers

• Approximate Robust Control of Uncertain Dynamical Systems (Dec 2018)

• Interval Prediction for Continuous-Time Systems with Parametric Uncertainties (Apr 2019)

• Practical Open-Loop Optimistic Planning (Apr 2019)

• α^α-Rank: Practically Scaling α-Rank through Stochastic Optimisation (Sep 2019)

• Social Attention for Autonomous Decision-Making in Dense Traffic (Nov 2019)

• Budgeted Reinforcement Learning in Continuous State Space (Dec 2019)

• Multi-View Reinforcement Learning (Dec 2019)

• Reinforcement learning for Dialogue Systems optimization with user adaptation (Dec 2019)

• Distributional Soft Actor Critic for Risk Sensitive Learning (Apr 2020)

• Bi-Level Actor-Critic for Multi-Agent Coordination (Apr 2020)

• Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes (Jun 2020)

• Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities (Jul 2020)

• Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems (Jul 2020)

• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction (Jul 2020)

• Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments (Aug 2020)

• B-GAP: Behavior-Guided Action Prediction for Autonomous Navigation (Nov 2020)

• Model-based Reinforcement Learning from Signal Temporal Logic Specifications (Nov 2020)

• Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs (Dec 2020)

• Assessing and Accelerating Coverage in Deep Reinforcement Learning (Dec 2020)

• Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing (Jan 2021)

• Interpretable Policy Specification and Synthesis through Natural Language and RL (Jan 2021)

• Deep Reinforcement Learning Techniques in Diversified Domains: A Survey (Feb 2021)

• Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles (Feb 2021)

• Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment (Feb 2021)

• Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

• Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning (Apr 2021)

• Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic (May 2021)

• Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling (Jun 2021)

• Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios (Jul 2021)

• Automatic Overtaking on Two-way Roads with Vehicle Interactions Based on Proximal Policy Optimization (Jul 2021)

• Robust Predictable Control (Sep 2021)

• Adapting Autonomous Agents for Automotive Driving Games (Nov 2021)

• Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network (Jul 2022)

• Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controller (Oct 2022)

• High-Level Decision-Making Non-player Vehicles (Nov 2022)

• Designing an Interpretability Analysis Framework for Deep Reinforcement Learning (DRL) Agents in Highway Automated Driving Simulation (Feb 2023)

• Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations (Mar 2023)

• Investigating High-Level Decision Making for Automated Driving (Apr 2023)

• Implementing Deep Reinforcement Learning (DRL)-based Driving Styles for Non-Player Vehicles (Nov 2023)

• Investigating Adversarial Policy Learning for Robust Agents in Automated Driving Highway Simulations (Jan 2024)

• Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation (Apr 2024)

• Validation of Reinforcement Learning Agents and Safety Shields with ProB (May 2024)

PhD Theses

• Reinforcement learning for Dialogue Systems optimization with user adaptation (2019)

• Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving (2020)

• Many-agent Reinforcement Learning (2021)

Master Theses

• Multi-Agent Reinforcement Learning with Application on Traffic Flow Control (Jun 2021)

• Deep Reinforcement Learning for Automated Parking (Aug 2021)

• Deep Reinforcement Learning and Imitation Learning for Autonomous Driving in a Minimalist Environment (Jun 2021)