gymnasium-cartpole-swingup 0.1.3

CartPole SwingUp environment for Gymnasium

Gymnasium CartPole SwingUp

A more challenging version of the classic CartPole environment for Gymnasium where the pole starts in a downward position.

Description

This package provides a port of the CartPole SwingUp environment to the modern Gymnasium API. It is based on:

• zuoxingdong/DeepPILCO
• hardmaru/estool

The environment has been updated to work with the latest Gymnasium interface and includes enhanced rendering capabilities.

Installation

# Using pip
pip install gymnasium-cartpole-swingup

# Using uv
uv add gymnasium-cartpole-swingup

Usage

import gymnasium as gym
import gymnasium_cartpole_swingup  # This import is required to register the environment, even if unused

# Create the environment
env = gym.make("CartPoleSwingUp-v0", render_mode="human")
observation, info = env.reset(seed=42)

for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)
    
    if terminated or truncated:
        observation, info = env.reset()

env.close()

Customizing Environment Parameters

You can customize the physics parameters of the environment by passing them to gym.make():

# Create an environment with custom parameters
env = gym.make(
    "CartPoleSwingUp-v0",
    render_mode="human",
    gravity=9.81,             # Gravitational acceleration (m/s²)
    cart_mass=1.0,            # Mass of the cart (kg)
    pole_mass=0.1,            # Mass of the pole (kg)
    pole_length=0.6,          # Length of the pole (m)
    force_mag=10.0,           # Force magnitude scale applied to cart
    friction=0.05,            # Friction coefficient
    x_threshold=2.5,          # Cart position limit (left/right boundary)
    cost_mode="default",      # Cost function mode ("default" or "pilco")
    sigma_c=0.25,             # Sigma parameter for PILCO cost function
)

Note: The import gymnasium_cartpole_swingup line is necessary to register the environment with Gymnasium, even though it may appear unused. If you're using auto-formatters or linters that remove unused imports, you can add a # noqa comment or disable that specific check:

import gymnasium_cartpole_swingup  # noqa: F401

Environment Details

• State: Initially, the pole hangs downward ($\theta \approx \pi$)
• Goal: Swing the pole upright and maintain balance
• Action Space: Force applied to cart $[-1, 1]$ (scaled to $[-10, 10]$ N internally)
• Observation Space: $[x, \dot{x}, \theta, \dot{\theta}]$
• Reward: Higher when pole is upright and cart is centered

Observation Space Detail

The observation is a 4-dimensional vector:

Index	Observation	Description	Min	Max
0	$x$	Cart position along the track	$-2.4$	$2.4$
1	$\dot{x}$	Cart velocity	$-\infty$	$\infty$
2	$\theta$	Angle of the pole	$-\pi$	$\pi$
3	$\dot{\theta}$	Angular velocity of the pole	$-\infty$	$\infty$

Notes:

• The angle $\theta$ is in radians and is kept within the range $[-\pi, \pi]$
• When the pole is upright, $\theta = 0$
• When the pole is hanging down, $\theta = \pi$ or $\theta = -\pi$

Action Space Detail

The action is a 1-dimensional continuous value:

Index	Action	Description	Min	Max
0	$F$	Horizontal force applied to the cart	$-1.0$	$1.0$

Notes:

• The force is scaled internally by a factor of $10.0$, resulting in an effective range of $[-10, 10]$ N
• Positive values move the cart to the right
• Negative values move the cart to the left

Reward Function

The environment supports two different reward (or cost) functions, which can be selected using the cost_mode parameter:

Default Mode (cost_mode="default")

The default reward function is a product of two components:

• Pole angle component: $\cos(\theta)$

  • Maximum value of $1.0$ when the pole is upright ($\theta = 0$)
  • Minimum value of $-1.0$ when the pole is hanging down ($\theta = \pi$ or $\theta = -\pi$)

• Cart position component: $\cos(x)$

  • Maximum value of $1.0$ when the cart is centered ($x = 0$)
  • Decreases as the cart moves away from center

Total reward = pole angle component $\times$ cart position component

PILCO Mode (cost_mode="pilco")

The PILCO (Probabilistic Inference for Learning COntrol) cost function is based on the squared distance between the pole tip position and the target position:

$reward = 1 - \exp(-\frac{d^2}{2\sigma_c^2})$

Where:

• $d$ is the Euclidean distance between the current pole tip position and the target (upright) position
• $\sigma_c$ is a parameter controlling the width of the cost function (default: 0.25)

This cost function is more focused on the pole tip position in Cartesian space rather than the angular position and cart position separately.

System Dynamics

The system dynamics follow the standard cart-pole physics model. The state update equations are:

$\ddot{x} = \frac{-2m_p l \dot{\theta}^2 \sin(\theta) + 3m_p g \sin(\theta)\cos(\theta) + 4F - 4b\dot{x}}{4(m_c + m_p) - 3m_p \cos^2(\theta)}$

$\ddot{\theta} = \frac{-3m_p l \dot{\theta}^2 \sin(\theta)\cos(\theta) + 6(m_c + m_p)g\sin(\theta) + 6(F - b\dot{x})\cos(\theta)}{4l(m_c + m_p) - 3m_p l \cos^2(\theta)}$

Where:

• $m_c = 0.5$ (kg): Mass of the cart (default)
• $m_p = 0.5$ (kg): Mass of the pole (default)
• $l = 0.6$ (m): Length of the pole (default)
• $g = 9.82$ (m/s²): Gravitational acceleration (default)
• $b = 0.1$: Friction coefficient (default)
• $F$: Applied force, scaled from action value to range $[-10, 10]$ N

All of these parameters can be customized when creating the environment as shown in the example above.