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Testing Action Space Clipping Implementation in OpenAI Gym

This test suite validates the ClipAction wrapper functionality in OpenAI Gym’s MountainCarContinuous environment. It ensures proper action space clipping behavior by comparing wrapped and unwrapped environment responses.

Test Coverage Overview

The test coverage focuses on verifying the ClipAction wrapper’s core functionality for action space limitation.

Key areas tested include:

Action value clipping within environment boundaries
Consistent environment responses between wrapped and unwrapped versions
Multiple action scenarios including boundary values and zero
State observation consistency
Reward calculation accuracy

Implementation Analysis

The testing approach uses direct comparison between wrapped and unwrapped environments to validate behavior consistency. The implementation employs numpy arrays for action inputs and leverages environment reset with fixed seeds to ensure reproducible testing conditions.

Technical patterns include:

Parallel environment execution
Numpy array comparison with allclose()
Systematic action space exploration
Deterministic state management

Technical Details

Testing tools and configuration:

Gym environment: MountainCarContinuous-v0
Primary wrapper: ClipAction
Testing library: Python’s built-in assert
Numpy for array operations
Environment seed control for reproducibility
Action space boundaries handling

Best Practices Demonstrated

The test implementation showcases several testing best practices for reinforcement learning environments.

Notable practices include:

Controlled environment initialization
Comprehensive action space coverage
Explicit state and reward verification
Deterministic test conditions
Clear test case organization
Efficient comparison methodology

openai/gym

tests/wrappers/test_clip_action.py

            
import numpy as np

import gym
from gym.wrappers import ClipAction


def test_clip_action():
    # mountaincar: action-based rewards
    env = gym.make("MountainCarContinuous-v0", disable_env_checker=True)
    wrapped_env = ClipAction(
        gym.make("MountainCarContinuous-v0", disable_env_checker=True)
    )

    seed = 0

    env.reset(seed=seed)
    wrapped_env.reset(seed=seed)

    actions = [[0.4], [1.2], [-0.3], [0.0], [-2.5]]
    for action in actions:
        obs1, r1, ter1, trunc1, _ = env.step(
            np.clip(action, env.action_space.low, env.action_space.high)
        )
        obs2, r2, ter2, trunc2, _ = wrapped_env.step(action)
        assert np.allclose(r1, r2)
        assert np.allclose(obs1, obs2)
        assert ter1 == ter2
        assert trunc1 == trunc2