Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
Abstract: Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges. However, developing autonomous vehicles need huge amount of training and testing before deploying it to real world. While the field of reinforcement learning (RL) has evolved into a powerful learning framework to the development of deep representation learning, and it is now capable of learning complicated policies in high-dimensional environments like in autonomous vehicles. In this regard, we make an effort, using Deep Q-Learning, to discover a method by which an autonomous car may maintain its lane at top speed while avoiding other vehicles. After that, we used CARLA simulation environment to test and verify our newly acquired policy based on the problem formulation.
- Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
- Human-level control through deep reinforcement learning. nature, 518(7540):529–533, 2015.
- Mastering the game of go with deep neural networks and tree search. Nature, 529:484–503, 2016.
- Learning to race: Experiments with a simulated race car. In The Florida AI Research Society, 1998.
- Inverse reinforcement learning with hybrid-weight trust-region optimization and curriculum learning for autonomous maneuvering. Technical report, Technical report, Department of Computer Science, University of Maryland, 2022.
- Driving in real life with inverse reinforcement learning. arXiv preprint arXiv:2206.03004, 2022.
- Watch this: Scalable cost-function learning for path planning in urban environments. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2089–2095. IEEE, 2016.
- Learning to drive using inverse reinforcement learning and deep q-networks. arXiv preprint arXiv:1612.03653, 2016.
- B-gap: Behavior-guided action prediction and navigation for autonomous driving. arXiv preprint arXiv:2011.03748, 2020.
- Overtaking maneuvers in simulated highway driving using deep reinforcement learning. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 1885–1890, 2018.
- Using graph-theoretic machine learning to predict human driver behavior. IEEE Transactions on Intelligent Transportation Systems, 23(3):2572–2585, 2022.
- Reinforcement learning: An introduction. MIT press, 2018.
- Carla: An open urban driving simulator. In Conference on robot learning, pages 1–16. PMLR, 2017.
- Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
- Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861–1870. PMLR, 2018.
- Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
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