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Testing MultiBinary Space Sampling Implementation in OpenAI Gym

This test suite validates the MultiBinary space implementation in OpenAI Gym’s spaces module. It focuses on testing the sampling functionality with various mask configurations and array shapes, ensuring correct binary value generation.

Test Coverage Overview

The test suite provides comprehensive coverage of the MultiBinary space sampling functionality.

Key areas tested include:

Single-dimensional binary array sampling with fixed masks
Conditional sampling with mixed mask values
Multi-dimensional array sampling with shaped masks
Edge cases for binary value constraints

Implementation Analysis

The testing approach uses NumPy arrays for mask definition and assertion verification. The implementation demonstrates systematic validation of the MultiBinary space’s sample() method, using both scalar and array-based initialization patterns.

Key patterns include:

NumPy array mask specifications
Explicit value assertions
Shape-based validations

Technical Details

Testing tools and configuration:

NumPy for array operations and comparisons
Python assert statements for validations
MultiBinary space from gym.spaces module
Int8 dtype specifications for mask arrays

Best Practices Demonstrated

The test implementation showcases several testing best practices for space validation.

Notable practices include:

Explicit test cases for different input scenarios
Clear separation of test conditions
Comprehensive assertion checks
Proper use of NumPy array operations

openai/gym

tests/spaces/test_multibinary.py

            
import numpy as np

from gym.spaces import MultiBinary


def test_sample():
    space = MultiBinary(4)

    sample = space.sample(mask=np.array([0, 0, 1, 1], dtype=np.int8))
    assert np.all(sample == [0, 0, 1, 1])

    sample = space.sample(mask=np.array([0, 1, 2, 2], dtype=np.int8))
    assert sample[0] == 0 and sample[1] == 1
    assert sample[2] == 0 or sample[2] == 1
    assert sample[3] == 0 or sample[3] == 1

    space = MultiBinary(np.array([2, 3]))
    sample = space.sample(mask=np.array([[0, 0, 0], [1, 1, 1]], dtype=np.int8))
    assert np.all(sample == [[0, 0, 0], [1, 1, 1]]), sample