policy
Car-Game-Reinforcement-Learning
Hharkheem · PyTorch
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Overview
Name
Car-Game-Reinforcement-Learning
Author
Hharkheem
Framework
PyTorch
License
MIT
Skill type
navigation
Evidence level
untested
Task description
The project implements a lane-based car game where an RL agent learns to navigate through traffic. The agent is trained using the Deep Q-Network (DQN) algorithm from Stable Baselines3.
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
3+17 mentioned but not in catalog yetCompatible environments
0No environments list Car-Game-Reinforcement-Learning yet.
Datasets that reference this policy
0No datasets reference Car-Game-Reinforcement-Learning yet.