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Human Learning Trajectories In Atari Games: A Comparison With Artificial Agents.pdf

Tsividis et al - Human Learning in Atari_0.pdf
Preview of Human Learning Trajectories in Atari Games: A Comparison with Artificial Agents
🔗 Source: cbmm.mit.edu
📊 Size: 844 KB
👤 Author: Padro A. Tsividis, Thomas Pouncy, Jacqueline L. Xu, Joshua B. Tenenbaum, Samuel J. Gershman
⬇️ Downloads: 39

Summary

investigates human learning trajectories on Atari games, comparing them to state-of-the-art reinforcement learning algorithms like Deep Q-Networks (DQN). Key findings include:

- Rapid Learning: Humans can learn to perform well on complex Atari games in minutes, while DQN algorithms require hundreds of hours.
- Cognitive Advantages: The study posits that humans' "start-up" software, including early-arising representations and rich prior knowledge about objects and the world, contributes to their speed of learning.
- Priors and Exploration: Strong priors specified at a general level, along with an imperative to explore and build theory-like models, are believed to facilitate rapid learning.
- Experimental Manipulations: The study manipulates prior knowledge about objects, game environment, and rules to test hypotheses about human learning mechanisms.
- Comparative Analysis: Human performance is compared to 'expert' human play and DQN performance at various experience levels, highlighting humans' superior learning rates in some games.

The authors conclude that while algorithms like DQN excel in asymptotic performance, humans exhibit unique learning capabilities driven by cognitive advantages and strategic exploration.

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Description

Research exploring how humans rapidly acquire skills in complex video games, contrasting with artificial agents that require extensive training. The study highlights the efficiency and adaptability of human learning.

Technical Information

  • File Format: PDF
  • File Size: 844 KB
  • Pages: 4
  • Language: EN
  • Author: Padro A. Tsividis, Thomas Pouncy, Jacqueline L. Xu, Joshua B. Tenenbaum, Samuel J. Gershman
  • Total Downloads: 39
  • Last Updated: 6 days ago

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