If you’re interested in diving into Reinforcement Learning, OpenAI Gym is one of the leading platforms for constructing environments that help train your agents. This tutorial serves as an introduction to the basic components of OpenAI Gym, helping you get up and running with it.

Prerequisites

Before starting, you’ll need a basic understanding of Python and access to the OpenAI Gym package.

Installation

To install OpenAI Gym, you can use either pip or conda. Here, we will opt for pip:

pip install -U gym

Environments

The core element of OpenAI Gym is the Env class, which acts as a simulator for the environment in which your agent will operate. OpenAI Gym comes pre-packaged with diverse environments, including ones for physics simulations and classic games. For example, the MountainCar environment challenges you to navigate a car up a hill, leveraging momentum.

import gymenv = gym.make('MountainCar-v0')

Interacting with the Environment

To interact with the environment, two key functions in the Env class are reset()—which reinitializes the environment—and step(action)—which executes an action and returns the new state, reward, and other info.

obs = env.reset()random_action = env.action_space.sample()new_obs, reward, done, info = env.step(random_action)

If the episode ends, use the reset() function again to start anew.

Spaces

In OpenAI Gym, both observations and actions are contained within specific structures known as "Spaces." The observation_space defines valid state representations, while the action_space specifies acceptable actions the agent can take.

obs_space = env.observation_spaceaction_space = env.action_spaceprint(f"The observation space: {obs_space}")print(f"The action space: {action_space}")

Wrappers

For enhancing the functionality of environments, OpenAI Gym provides the Wrapper class, allowing you to modify or add new features systematically. For instance, you might want to normalize inputs or clip rewards.

class MyWrapper(gym.Wrapper):    def __init__(self, env):        super(MyWrapper, self).__init__(env)    def reset(self):        return super(MyWrapper, self).reset()    def step(self, action):        obs, reward, done, info = super(MyWrapper, self).step(action)        return obs, reward, done, info

Vectorized Environments

For efficiency, especially in Deep RL, running multiple environments in parallel is beneficial. OpenAI’s Baselines library supports this functionality through the SubprocVecEnv.

from baselines.common.vec_env.subproc_vec_env import SubprocVecEnvnum_envs = 3envs = SubprocVecEnv([lambda: gym.make('BreakoutNoFrameskip-v4') for _ in range(num_envs)])init_obs = envs.reset()

Conclusion

With this framework, you should be well equipped to start training reinforcement learning agents using OpenAI Gym. If the environment you need isn’t available, you can also create custom environments. This tutorial serves as a stepping stone into the exciting world of reinforcement learning!

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