Summary of Self-driving Car Racing: Application Of Deep Reinforcement Learning, by Florentiana Yuwono et al.
Self-Driving Car Racing: Application of Deep Reinforcement Learning
by Florentiana Yuwono, Gan Pang Yen, Jason Christopher
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores applying deep reinforcement learning (RL) techniques to autonomous self-driving car racing, aiming to develop an AI agent that efficiently drives a simulated car in OpenAI Gymnasium CarRacing. Various RL algorithms are investigated, including DQN, PPO, and adaptations incorporating transfer learning and recurrent neural networks (RNNs). The project demonstrates the effectiveness of combining ResNet and LSTM models for capturing complex spatial and temporal dynamics, while PPO shows promise for fine control in continuous action spaces. However, policy collapse remains a challenge. Comparing performances and outlining future research directions contribute to AI system development in autonomous driving and related control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can help self-driving cars learn to race better. Researchers used special computer programs called deep reinforcement learning (RL) to teach an AI agent to drive a simulated car in a racing game. They tested different types of RL, including some that combined ideas from other AI techniques like transfer learning and memory. The results show that by combining these ideas, the AI can learn to understand complex things about the racing environment. This could be useful for making self-driving cars better at navigating real-world roads. |
Keywords
» Artificial intelligence » Lstm » Reinforcement learning » Resnet » Transfer learning
