ACE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments

ACE-Bench is an AI agent evaluation framework that reduces overhead and provides configurable, scalable, and controllable assessment. It helps developers iterate faster and gain clearer insights into agent performance across varied difficulties and task lengths.

intermediate30 min5 steps

The play

Initiate an ACE-Bench Evaluation
Begin by defining the core parameters for your AI agent evaluation using ACE-Bench, focusing on the agent(s) you wish to assess and the general evaluation goal.
Configure Agent-Specific Scenarios
Utilize ACE-Bench's 'Agent Configurable Evaluation' feature to tailor assessment scenarios. Define specific conditions, environments, and metrics relevant to your agent's capabilities and design objectives.
Set Scalable Task Horizons
Implement 'Scalable Horizons' to adapt evaluation tasks to varying complexities and lengths. Specify the range or specific values for task duration or depth to thoroughly test agent performance under different temporal constraints.
Adjust Controllable Difficulty Levels
Leverage 'Controllable Difficulty' to precisely tune the challenge level of your evaluation tasks. Define difficulty parameters (e.g., number of obstacles, complexity of decision-making, resource scarcity) to create a robust and fair assessment.
Execute in Lightweight Environments
Run your configured evaluations within ACE-Bench's 'Lightweight Environments'. This ensures reduced computational and time costs, allowing for faster iteration and more efficient benchmarking cycles.

Starter code

evaluation_config:
  agent_id: "my_reinforcement_agent_v2.1"
  evaluation_type: "performance_benchmark"
  scenario:
    name: "resource_gathering_challenge"
    parameters:
      map_size: "medium"
      initial_resources: 100
      enemy_presence: "low"
  horizon_settings:
    type: "scalable"
    min_steps: 100
    max_steps: 500
    increment: 100
  difficulty_settings:
    level: "intermediate"
    factors:
      environmental_variability: 0.6
      task_complexity: 0.7
  metrics_to_track:
    - "total_reward"
    - "actions_per_episode"
    - "failure_rate"

Source

Paperarxiv.org