AI Agent Benchmarking Task

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).

Context:
- It can (typically) involve performance metrics such as accuracy, efficiency, and robustness.
- It can (often) be conducted in simulation environments or in real-world scenarios to test the adaptability and scalability of AI Agents.
- It can range from simple tasks like pathfinding and object recognition to complex decision-making and learning scenarios.
- It can utilize Standard Datasets or dynamically generated tests to evaluate the agents comprehensively.
- It can be supported by an AI Agent Benchmarking System.
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Example(s):
- An AI Safety Benchmarking Event assesses AI agents against safety and ethical performance criteria.
- A RoboCup Competition for soccer-playing AI agents.
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Counter-Example(s):
- NLP Benchmarking, such as: a SQuAD (Stanford Question Answering Dataset).
- Manual Task Performance Evaluations, which involve human rather than artificial agents.
- Software Debugging Tasks, which focus on identifying and fixing errors in code rather than evaluating performance across a range of tasks.
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See: AI Agent, Benchmarking Task, Performance Metric, Simulation Environment.

References

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.

(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.