Deep Reinforcement Learning based Decision-Making in Autonomous Driving Tasks
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Updated
Jan 31, 2026 - Jupyter Notebook
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.
Interpretability in Autonomous Driving: Visual Attribution Analysis of RL Agents
Reinforcement Learning : Autonomous parallel parking task. implementing SAC and DreamerV3's World Model on Highway-env
Development of autonomous agents driving safely in highway environment 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.
using reinforcement learning
PPO agent trained to navigate highway-env - mean reward 20.7, zero crashes, with reward curve and video evaluation
决策规划学习笔记:highway-env 多场景 + 规则Agent + YOLO感知叠加
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.
Decision Arena: TypeSafe's Jev vs open-source Laya playing highway-env, Snake and Blackjack with zero training, plus benchmarks and a Claude Code watchdog
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
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