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## A2Perf is a benchmarking suite for evaluating agents on sequential decision-making problems that are relevant to the real world.

This library contains a collection of environments from domains spanning computer chip-floorplanning, web navigation, and quadruped locomotion.

The Gymnasium interface allows users to initialize and interact with the A2Perf environments as follows:

    import gymnasium as gym
    from a2perf.domains import circuit_training
    # from a2perf.domains import web_navigation
    # from a2perf.domains import quadruped_locomotion

    # Choose one of the A2Perf environments
    env = gym.make("CircuitTraining-Ariane-v0")
    # or env = gym.make("WebNavigation-Difficulty-01-v0")
    # or env = gym.make("QuadrupedLocomotion-DogPace-v0")

    observation, info = env.reset(seed=42)
    for _ in range(1000):
        action = env.action_space.sample()  # Replace with your policy
        observation, reward, terminated, truncated, info = env.step(action)
        if terminated or truncated:
            observation, info = env.reset()
    env.close()