Interacting with Gymnasium environments

Reinforcement Learning with Gymnasium in Python

Fouad Trad

Machine Learning Engineer

Gymnasium

  • Standard library for RL tasks
  • Abstracts complexity of RL problems
  • Provides a plethora of RL environments

Image showing the gymnasium logo along with some environments it provides.

Reinforcement Learning with Gymnasium in Python

Key Gymnasium environments

CartPole: Agent must balance a pole on moving cart GIF representing the CartPole environment.

MountainCar: Agent must drive a car up a steep hill GIF representing the MountainCar environment.

FrozenLake: Agent must navigate a frozen lake with holes GIF representing the FrozenLake gymnasium environment.

Taxi: Picking up and dropping off passengers GIF representing the Taxi environment.

Reinforcement Learning with Gymnasium in Python

Gymnasium interface

 

  • Unified for all environments
  • Includes functions and methods to:
    • Initialize environment
    • Visually represent environment
    • Execute actions
    • Observe outcomes

GIF representing the CartPole environment.

Reinforcement Learning with Gymnasium in Python

Creating and initializing the environment

import gymnasium as gym


env = gym.make('CartPole', render_mode='rgb_array')

state, info = env.reset(seed=42)
print(state)

[-0.04405273  0.0242996  -0.04377224 -0.01767325]
1 https://gymnasium.farama.org/environments/classic_control/cart_pole/
Reinforcement Learning with Gymnasium in Python

Visualizing the state

 

import matplotlib.pyplot as plt

state_image = env.render()
plt.imshow(state_image)

plt.show()

Plot of the initial state in CartPole.

Reinforcement Learning with Gymnasium in Python

Visualizing the state

 

import matplotlib.pyplot as plt


def render():
  state_image = env.render()
  plt.imshow(state_image)
  plt.show()


# Call function
render()

Plot of the initial state in CartPole.

Reinforcement Learning with Gymnasium in Python

Performing actions

  • 0: moving left
  • 1: moving right
action = 1 
state, reward, terminated, truncated, info = env.step(action)
Reinforcement Learning with Gymnasium in Python

Performing actions

  • 0: moving left
  • 1: moving right
action = 1
state, reward, terminated, _, _ = env.step(action)


print("State: ", state)
print("Reward: ", reward)
print("Terminated: ", terminated)

State:  [-0.04356674  0.22002107 -0.0441257  -0.3238392 ]
Reward:  1.0
Terminated:  False
Reinforcement Learning with Gymnasium in Python

Interaction loops

while not terminated:
    action = 1 # Move to the right
    state, reward, terminated, _, _ = env.step(action)
    render()

Image showing four different states in the CartPole environment, captured during the interaction loop.

Reinforcement Learning with Gymnasium in Python

Let's practice!

Reinforcement Learning with Gymnasium in Python

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