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)
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)
Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
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)
Robust Predictable Control (Sep 2021)
Adapting Autonomous Agents for Automotive Driving Games (Nov 2021)
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)
Validation of Reinforcement Learning Agents and Safety Shields with ProB (May 2024)