Summary of Learning to Play Atari Games Using Dueling Q-learning and Hebbian Plasticity, by Md Ashfaq Salehin
Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity
by Md Ashfaq Salehin
First submitted to arxiv on: 22 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 presents an advanced deep reinforcement learning architecture that trains neural network agents to play Atari games from raw game pixels, action space, and reward information. The system uses techniques like deep Q-networks and dueling Q-networks to train efficient agents, similar to those used by DeepMind to beat human players. As an extension, the paper explores the feasibility of plastic neural networks as agents in this scenario. Plasticity is implemented using backpropagation and the Hebbian update rule, enabling lifelong learning after initial training. This work provides valuable insights for future studies on adaptive learning environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to train computer programs to play classic video games like Pong and Space Invaders. They used special algorithms that help the programs learn from trial and error, just like humans do. The new method allows the programs to keep improving even after they’ve already learned how to play the game well. This could be important for creating artificial intelligence systems that can learn and adapt in real-world situations. |
Keywords
» Artificial intelligence » Backpropagation » Neural network » Reinforcement learning
