highway-env

Deep Reinforcement Learning based Decision-Making in Autonomous Driving Tasks

Heterogeneous Multi-agent Version of Highway-env

An extension of the Planner-Actor-Reporter framework applied to autonomous vehicles in Highway-Env and CARLA.

Autonomous Driving W/ Deep Reinforcement Learning in Lane Keeping - DDQN and SAC with kinematics/birdview-images

Implementation of Deep Deterministic Policy Gradient (DDPG) method on autonomous vehicle within the highway-env

Reinforcement Learning Final Project

stress testing black-box AVs with MARL

DQN-based autonomous driving agent on Highway-Env. Trained with Stable-Baselines3. Balances speed, collision avoidance, and lane discipline.

🚗 Analyze and visualize decision-making in autonomous driving RL agents using Integrated Gradients for clearer interpretability in complex driving tasks.

Reinforcement Learning : Autonomous parallel parking task. implementing SAC and DreamerV3's World Model on Highway-env

Interpretability in Autonomous Driving: Visual Attribution Analysis of RL Agents

Development of autonomous agents driving safely in highway environment using Reinforcement Learning

using reinforcement learning

Three scripts that train DQN and PPO at one traffic density in highway-env, evaluate them at three, and write the results to a CSV and figures.

PPO agent trained to navigate highway-env - mean reward 20.7, zero crashes, with reward curve and video evaluation

RL/IL/Diffusion baselines for autonomous-driving decision-making in highway-env (PPO, SAC; CARLA & Diffusion Policy planned)

Predicting, detecting, and containing V2V misinformation cascades in connected vehicle fleets — runtime failure-path monitor validated on highway-env, SUMO, and CARLA

决策规划学习笔记：highway-env 多场景 + 规则Agent + YOLO感知叠加

Text-to-Reward RL for highway-env: an LLM writes, validates, and evolves complete Python reward functions for a PPO agent, with built-in reward-hacking detection.

A reward-free autonomous driving agent in highway-env that plans by counting survivable futures, exploring the limits of viability theory when survival and task progression diverge.