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- W4310555087 abstract "Training self-driving cars is often challenging since they require a vast amount of labeled data in multiple real-world contexts, which is computationally and memory intensive. Researchers often resort to driving simulators to train the agent and transfer the knowledge to a real-world setting. Since simulators lack realistic behavior, these methods are quite inefficient. To address this issue, we introduce a framework (perception, planning, and control) in a real-world driving environment that transfers the real-world environments into gaming environments by setting up a reliable Markov Decision Process (MDP). We propose variations of existing Reinforcement Learning (RL) algorithms in a multi-agent setting to learn and execute the discrete control in real-world environments. Experiments show that the multi-agent setting outperforms the single-agent setting in all the scenarios. We also propose reliable initialization, data augmentation, and training techniques that enable the agents to learn and generalize to navigate in a real-world environment with minimal input video data, and with minimal training. Additionally, to show the efficacy of our proposed algorithm, we deploy our method in the virtual driving environment TORCS." @default.
- W4310555087 created "2022-12-12" @default.
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- W4310555087 date "2022-11-28" @default.
- W4310555087 modified "2023-09-26" @default.
- W4310555087 title "Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning" @default.
- W4310555087 doi "https://doi.org/10.48550/arxiv.2211.15920" @default.
- W4310555087 hasPublicationYear "2022" @default.
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