Introduction

OpenAI's Gym is an extensive toolkit for developing and comparing reinforcement learning algorithms. Environments in Gym provide an interface similar to video games with a set of actions, states, and rewards. Over time, you may find existing environments limiting or may need them to be customized for very specific use cases. This article gives a detailed explanation of how to create a new Gym environment to tailor your reinforcement learning experiments.

Key Components of a Gym Environment

When creating a new Gym environment, you need to define several key components:

1. Spaces:
   • Action Space: Defines what actions are possible in the environment.
   • Observation Space: Represents the state space of the environment.
2. Methods:
   • __init__: Initialization method to set up the environment and define action and observation spaces.
   • reset: Resets the environment to an initial state.
   • step: Executes an action in the environment, returns the new state, reward, done flag, and additional information.
   • render: (optional) Provides a visualization of the environment.

Step-by-Step Guide to Creating a New Gym Environment

Step 1: Import Gym

First, ensure you have Gym installed. If not, you can install it using pip:

pip install gym

Now import the required Gym components:

import gym
from gym import spaces
import numpy as np

Step 2: Define Your Environment Class

Create a new environment class that inherits from gym.Env.

1class CustomEnv(gym.Env):
2    def __init__(self):
3        super(CustomEnv, self).__init__()
4        # Define action and observation space
5        # Example: Discrete action space with 2 actions
6        self.action_space = spaces.Discrete(2)
7        
8        # Example: Box observation space with bounds and shape
9        self.observation_space = spaces.Box(low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32)
10    
11    def reset(self):
12        # Reset the state of the environment to an initial state
13        initial_state = np.random.rand()  # Example: random initial state
14        return np.array([initial_state], dtype=np.float32)
15    
16    def step(self, action):
17        # Execute one time step within the environment
18        state = np.random.rand()  # Example: random next state
19        reward = 1.0 if action == 1 else 0.0  # Example: reward based on action
20        done = np.random.rand() > 0.95  # Example: 5% chance to end the episode
21        return np.array([state], dtype=np.float32), reward, done, {}
22    
23    def render(self, mode='human'):
24        # Print the current state
25        pass

Step 3: Register Your Environment

Register the environment using Gym's registry. This step makes your environment recognizable by Gym.

1from gym.envs.registration import register
2
3register(
4    id='CustomEnv-v0',
5    entry_point='path.to.module:CustomEnv',
6    max_episode_steps=100,
7)

Make sure to replace 'path.to.module:CustomEnv' with the actual path to your custom environment class.

Step 4: Test Your Environment

After registering, you can test your environment by creating an instance and running a simple loop.

1env = gym.make('CustomEnv-v0')
2state = env.reset()
3done = False
4
5while not done:
6    action = env.action_space.sample()  # Example: Sample a random action
7    state, reward, done, info = env.step(action)
8    env.render()
9    
10env.close()

Summary Table

Below is a table summarizing the key methods and components when creating a new Gym environment:

Additional Considerations

Custom Observations and Actions

You may opt for more complex observation and action spaces. Use spaces.Tuple and spaces.Dict to compose multiple spaces.

Environment Configuration

Consider parameterizing your environment using a configuration file or through parameters in the initializer for variability and to increase test coverage.

Debugging and Testing

Use assertions and logging to ensure environment methods operate as expected, especially within reset and step. Write unit tests to automate this verification process.

Performance Considerations

When environments become slow or computationally heavy, it may preclude benchmarking real-world algorithms. Profile your code and optimize its main loop, especially where it interacts with Gym interface methods like step.

Through careful construction of a custom Gym environment, you can better suit complex reinforcement learning needs and facilitate advancements tailored to very specific experimental goals.