AI Agent Benchmarking Task

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An AI Agent Benchmarking Task is a AI benchmarking task for AI agent evaluation (that involves the systematic evaluation of AI agents to assess their performance on specific metrics in a controlled environment).



References

2024

  • https://youtube.com/watch?v=YZp3Hy6YFqY
    • NOTES
      • Benchmarking AI Agent can evaluate the performance of AI agents across various operating systems and applications, ensuring they perform tasks correctly and efficiently in a controlled environment.
      • It can simulate real-world scenarios to test the AI agents' ability to understand and execute complex instructions, thus providing developers with actionable insights to improve agent capabilities.
      • It can facilitate continuous improvement of AI systems by providing structured feedback and metrics on their performance, enabling iterative enhancements and adjustments to the agents' algorithms and interactions.

2020

  • (Badia et al., 2020) ⇒ Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell. (2020). “Agent57: Outperforming the Atari Human Benchmark.” In: International Conference on Machine Learning, pp. 507-517. PMLR.
    • QUOTE: "… benchmark in the reinforcement learning (RL) community for the past decade. This benchmark … , the first deep RL agent that outperforms the standard human benchmark on all 57 Atari …"
    • ABSTRACT: Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.