Metadata-Version: 2.4
Name: kaist-or-gym
Version: 0.1.3
Summary: Custom Gym/Gymnasium environments for operations research problems
Author-email: Kihyuk Hong <kihyukh@kaist.ac.kr>
License-Expression: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: gymnasium>=1.2.0
Requires-Dist: numpy>=1.23
Requires-Dist: matplotlib>=3.0
Requires-Dist: ipython==7.34.0
Provides-Extra: dev
Requires-Dist: pytest>=7; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest>=7; extra == "test"
Requires-Dist: gymnasium[other]>=0.29; extra == "test"

## Usage Example

Below is a minimal example of how to use the `TrafficControlEnv` environment for a fixed number of time steps:

```python
import gymnasium as gym
import kaist_or_gym

# Create the environment
env = gym.make("kaist-or/TrafficControlEnv-v0", render_mode="human")

observation, info = env.reset()

for _ in range(100):  # Run for 100 time steps
    action = env.action_space.sample()  # Replace with your policy
    observation, reward, terminated, truncated, info = env.step(action)
    env.render()
    if terminated or truncated:
        break

env.close()
```

This example demonstrates how to create the environment, take random actions, render the intersection, and run for a fixed number of steps.
